MCP Radar
MCP 서버

Islam West Africa Collection (IWAC)

io.github.fmadore/iwac-mcp-server
데이터 및 분석 지식 및 문서 검색 및 리서치 공개 · 연결 가능 MCP 2025-11-25

이 MCP로 할 수 있는 일

Provides read-only search, retrieval, archival metadata, OCR, transcription, geographic, temporal, sentiment, topic, and semantic analysis for the Islam West Africa Collection.

기능

도구

34 도구
fetch
Fetch IWAC item
Retrieve the full text and metadata of one IWAC item by an id returned from `search` (format '<category>:<number>', e.g. 'articles:28576'). Returns {id, title, text, url, metadata}: `text` is the item's OCR / abstract / transcription / description, `url` is the canonical islam.zmo.de link to cite, and `metadata` holds the remaining fields (author, date, country, newspaper, AI sentiment, …). Categories: articles, publications, references, documents, index, audiovisual, images.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['id'], 'properties': {'id': {'type': 'string', 'description': "Item id from search, e.g. 'articles:28576' or 'references:11045'"}}}
출력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['id', 'text', 'category', 'metadata'], 'properties': {'id': {'type': 'string'}, 'url': {'type': 'string'}, 'text': {'type': 'string'}, 'title': {'type': 'string'}, 'category': {'type': 'string'}, 'metadata': {'type': 'object', 'properties': {}, 'additionalProperties': {}}, 'text_source': {'type': 'string'}, 'text_truncated': {'type': 'boolean'}, 'recommended_tool': {'type': 'string'}, 'recommended_usage': {'type': 'object', 'properties': {}, 'additionalProperties': {}}}, 'additionalProperties': False}
get_article
Get article details
Get one article (by id): full metadata, the AI abstract (description_ai), AI sentiment, and OCR text. Pass a `keyword` to get ~2000-char excerpts around each match instead of the full (capped) OCR.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['article_id'], 'properties': {'keyword': {'type': 'string', 'description': 'Return excerpts around matches instead of the full OCR (accent-insensitive)'}, 'article_id': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991}, 'max_excerpts': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991, 'description': 'Default 10, max 25'}, 'context_chars': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991, 'description': 'Default 2000, max 5000'}}}
get_audiovisual
Get audiovisual details
Get one audiovisual record by id: full description and transcription (where one exists), creator/publishing channel, duration, medium, subjects, places, language, rights, source, and three distinct links — `url` (the IWAC page, the one to cite), `external_url` (where a harvested video plays) and `media_url` (a deposited file). `source_type` says which to expect.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['audiovisual_id'], 'properties': {'audiovisual_id': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991}}}
get_collection_stats
Collection statistics
Overall statistics for every IWAC subset, including `fulltext_coverage` — how many items in each subset actually carry searchable full text in this public dataset. Read that before treating any keyword count as a full-text census.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'properties': {}}
출력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['view', 'collection_name', 'dataset_url', 'subset_counts', 'total_records'], 'properties': {'view': {'type': 'string'}, 'date_range': {'type': 'object', 'required': ['earliest', 'latest'], 'properties': {'latest': {'type': 'string'}, 'earliest': {'type': 'string'}}, 'additionalProperties': {}}, 'dataset_url': {'type': 'string'}, 'fulltext_note': {'type': 'string'}, 'subset_counts': {'type': 'object', 'propertyNames': {'type': 'string'}, 'additionalProperties': {'type': 'number'}}, 'total_records': {'type': 'number'}, 'failed_subsets': {'type': 'array', 'items': {'type': 'string'}}, 'collection_name': {'type': 'string'}, 'newspaper_count': {'type': 'number'}, 'fulltext_coverage': {'type': 'object', 'propertyNames': {'type': 'string'}, 'additionalProperties': {'type': 'object', 'properties': {}, 'additionalProperties': {}}}, 'articles_by_country': {'type': 'object', 'propertyNames': {'type': 'string'}, 'additionalProperties': {'type': 'number'}}}, 'additionalProperties': False}
get_cooccurrence
Co-occurrence matrix
How often the top values of a multi-valued field appear on the SAME item — a subject/place co-mention matrix. Answers 'what is X discussed alongside' without reading anything: the pair counts are the structure of the tagging. Returns the top values, the full symmetric matrix (diagonal = each value's own count) and the strongest pairs.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'properties': {'field': {'type': 'string', 'description': 'subject (default) | spatial | author | language'}, 'top_n': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991, 'description': 'Values on each axis (default 15, max 30)'}, 'subset': {'type': 'string', 'description': 'articles (default) | publications | references'}, 'country': {'type': 'string', 'description': "Exact country name: Benin | Burkina Faso | Côte d'Ivoire | Niger | Nigeria | Togo (accents optional)"}, 'date_to': {'type': 'string', 'description': 'YYYY-MM-DD (or YYYY)'}, 'keyword': {'type': 'string', 'description': "ONE French concept keyword; substring over the subset's text fields"}, 'subject': {'type': 'string', 'description': 'Exact subject tag (pipe-aware)'}, 'date_from': {'type': 'string', 'description': 'YYYY-MM-DD (or YYYY)'}, 'newspaper': {'type': 'string', 'description': 'Newspaper (articles) or periodical/series title (publications)'}}}
출력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['view', 'subset', 'field', 'filters', 'total_matches', 'values', 'matrix', 'top_pairs'], 'properties': {'note': {'type': 'string'}, 'view': {'type': 'string'}, 'field': {'type': 'string'}, 'matrix': {'type': 'array', 'items': {'type': 'array', 'items': {'type': 'number'}}}, 'subset': {'type': 'string'}, 'values': {'type': 'array', 'items': {'type': 'object', 'properties': {}, 'additionalProperties': {}}}, 'filters': {'type': 'object', 'properties': {}, 'additionalProperties': {}}, 'top_pairs': {'type': 'array', 'items': {'type': 'object', 'properties': {}, 'additionalProperties': {}}}, 'total_matches': {'type': 'number'}}, 'additionalProperties': False}
get_country_comparison
Compare countries
Compare article counts, newspaper counts, date ranges, and gpt-5-6-luna polarity across countries.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'properties': {}}
출력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['view', 'total_countries', 'countries'], 'properties': {'view': {'type': 'string'}, 'countries': {'type': 'array', 'items': {'type': 'object', 'properties': {}, 'additionalProperties': {}}}, 'polarity_model': {'type': 'string'}, 'total_countries': {'type': 'number'}}, 'additionalProperties': False}
get_document
Get document details
Get one archival document (by id): full metadata, AI description, and OCR text. Pass a `keyword` to get ~2000-char excerpts around each match instead of the full (capped) OCR — useful for long documents.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['document_id'], 'properties': {'keyword': {'type': 'string', 'description': 'Return excerpts around matches instead of the full OCR (accent-insensitive)'}, 'document_id': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991}, 'max_excerpts': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991, 'description': 'Default 10, max 25'}, 'context_chars': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991, 'description': 'Default 2000, max 5000'}}}
get_field_distribution
Rank a field's values
Rank the values of one multi-valued field across a filtered set — the direct way to answer 'which places does this coverage name most', 'who signs these articles', 'what subjects dominate'. Pipe-joined fields (subject, spatial, author, language, country) are split, so an article tagged 'Prière|Ramadan' counts once for each. Optional over_time adds the per-year share of items that carry ANY value for the field, which is how you see e.g. bylines appearing as the press professionalises.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['field'], 'properties': {'field': {'type': 'string', 'description': 'subject | spatial | author | language | newspaper | country'}, 'top_n': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991, 'description': 'Values returned (default 25, max 100)'}, 'subset': {'type': 'string', 'description': 'articles (default) | publications | references'}, 'country': {'type': 'string', 'description': "Exact country name: Benin | Burkina Faso | Côte d'Ivoire | Niger | Nigeria | Togo (accents optional)"}, 'date_to': {'type': 'string', 'description': 'YYYY-MM-DD (or YYYY)'}, 'keyword': {'type': 'string', 'description': "ONE French concept keyword; substring over the subset's text fields"}, 'subject': {'type': 'string', 'description': 'Exact subject tag (pipe-aware)'}, 'date_from': {'type': 'string', 'description': 'YYYY-MM-DD (or YYYY)'}, 'newspaper': {'type': 'string', 'description': 'Newspaper (articles) or periodical/series title (publications)'}, 'over_time': {'type': 'boolean', 'description': 'Also return the per-year share of items carrying a value'}}}
출력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['view', 'subset', 'field', 'filters', 'total_matches', 'items_with_value', 'distinct_values', 'values'], 'properties': {'note': {'type': 'string'}, 'view': {'type': 'string'}, 'field': {'type': 'string'}, 'subset': {'type': 'string'}, 'values': {'type': 'array', 'items': {'type': 'object', 'properties': {}, 'additionalProperties': {}}}, 'filters': {'type': 'object', 'properties': {}, 'additionalProperties': {}}, 'other_values': {'type': 'number'}, 'total_matches': {'type': 'number'}, 'distinct_values': {'type': 'number'}, 'coverage_by_year': {'type': 'object', 'propertyNames': {'type': 'string'}, 'additionalProperties': {'type': 'object', 'properties': {}, 'additionalProperties': {}}}, 'items_with_value': {'type': 'number'}}, 'additionalProperties': False}
get_image
Get photograph details
Get one photograph by id: title, photographer, capture date, place and coordinates, subjects, rights, the IIIF manifest, and the full-resolution `image_url`. The server returns URLs, not image bytes.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['image_id'], 'properties': {'image_id': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991}}}
get_index_entry
Get index entry details
Get full details of an index entry by id (raw dataset columns, French names — Titre, Prénom, Coordonnées…).
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['entry_id'], 'properties': {'entry_id': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991}}}
get_lexical_metrics
Press language metrics
Readability, lexical richness and length of the press text, averaged by year, newspaper or country. `Lisibilite_OCR` is a French readability score (higher = easier); `Richesse_Lexicale_OCR` is MATTR, a moving-average type-token ratio that is ALREADY length-robust — do not normalise it by word count or bin it by length. Readability is computed against a French lexicon, so non-French items are excluded from that metric (and counted in readability_excluded) rather than reported as unreadable; MATTR and word count need no lexicon and cover everything. Only items whose full text ships in this public dataset carry these columns at all.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'properties': {'top_n': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991, 'description': 'Groups returned when grouping by newspaper (default 20, max 60)'}, 'country': {'type': 'string', 'description': "Exact country name: Benin | Burkina Faso | Côte d'Ivoire | Niger | Nigeria | Togo (accents optional)"}, 'date_to': {'type': 'string', 'description': 'YYYY-MM-DD (or YYYY)'}, 'keyword': {'type': 'string', 'description': "ONE French concept keyword; substring over the subset's text fields"}, 'subject': {'type': 'string', 'description': 'Exact subject tag (pipe-aware)'}, 'group_by': {'type': 'string', 'description': 'year (default) | newspaper | country'}, 'date_from': {'type': 'string', 'description': 'YYYY-MM-DD (or YYYY)'}, 'newspaper': {'type': 'string', 'description': 'Newspaper (articles) or periodical/series title (publications)'}}}
출력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['view', 'group_by', 'filters', 'total_matches', 'groups', 'metrics'], 'properties': {'note': {'type': 'string'}, 'view': {'type': 'string'}, 'groups': {'type': 'array', 'items': {'type': 'object', 'properties': {}, 'additionalProperties': {}}}, 'filters': {'type': 'object', 'properties': {}, 'additionalProperties': {}}, 'metrics': {'type': 'object', 'propertyNames': {'type': 'string'}, 'additionalProperties': {'type': 'object', 'properties': {}, 'additionalProperties': {}}}, 'group_by': {'type': 'string'}, 'total_matches': {'type': 'number'}, 'readability_excluded': {'type': 'number'}}, 'additionalProperties': False}
get_newspaper_stats
Newspaper statistics
Per-newspaper article counts and date ranges.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'properties': {'country': {'type': 'string', 'description': "Exact country name: Benin | Burkina Faso | Côte d'Ivoire | Niger | Togo (accents optional)"}}}
출력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['view', 'total_newspapers', 'total_articles', 'newspapers'], 'properties': {'view': {'type': 'string'}, 'newspapers': {'type': 'array', 'items': {'type': 'object', 'properties': {}, 'additionalProperties': {}}}, 'country_filter': {'type': 'string'}, 'total_articles': {'type': 'number'}, 'total_newspapers': {'type': 'number'}}, 'additionalProperties': False}
get_place_distribution
Places on a map
Places named by a filtered set of items, joined to the index's authority records so each carries coordinates where the index has them. Use this rather than get_field_distribution when the question is geographic — where coverage clusters — and the plain ranking when it is not. Only `Lieux` index entries are geocoded (555 of 683); persons, organisations and events carry no coordinates and never will, and any named place with no index entry comes back under `ungeocoded` rather than being dropped.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'properties': {'top_n': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991, 'description': 'Geocoded places returned (default 60, max 200)'}, 'subset': {'type': 'string', 'description': 'articles (default) | publications | references'}, 'country': {'type': 'string', 'description': "Exact country name: Benin | Burkina Faso | Côte d'Ivoire | Niger | Nigeria | Togo (accents optional)"}, 'date_to': {'type': 'string', 'description': 'YYYY-MM-DD (or YYYY)'}, 'keyword': {'type': 'string', 'description': "ONE French concept keyword; substring over the subset's text fields"}, 'subject': {'type': 'string', 'description': 'Exact subject tag (pipe-aware)'}, 'date_from': {'type': 'string', 'description': 'YYYY-MM-DD (or YYYY)'}, 'newspaper': {'type': 'string', 'description': 'Newspaper (articles) or periodical/series title (publications)'}}}
출력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['view', 'subset', 'filters', 'total_matches', 'items_with_place', 'places'], 'properties': {'note': {'type': 'string'}, 'view': {'type': 'string'}, 'places': {'type': 'array', 'items': {'type': 'object', 'properties': {}, 'additionalProperties': {}}}, 'subset': {'type': 'string'}, 'filters': {'type': 'object', 'properties': {}, 'additionalProperties': {}}, 'ungeocoded': {'type': 'array', 'items': {'type': 'object', 'properties': {}, 'additionalProperties': {}}}, 'total_matches': {'type': 'number'}, 'items_by_country': {'type': 'object', 'propertyNames': {'type': 'string'}, 'additionalProperties': {'type': 'number'}}, 'items_with_place': {'type': 'number'}, 'ungeocoded_mentions': {'type': 'number'}}, 'additionalProperties': False}
get_publication_fulltext
Get publication full text
Full OCR text of a publication, optionally returning ~2000-char excerpts around keyword matches (accent-insensitive; capped — see match_count vs excerpts_returned).
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['publication_id'], 'properties': {'keyword': {'type': 'string'}, 'max_excerpts': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991, 'description': 'Default 10, max 25'}, 'context_chars': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991, 'description': 'Default 2000, max 5000'}, 'publication_id': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991}}}
get_reference
Get reference details
Full bibliographic record for one academic reference (by id), including the complete abstract (present for ~51% of references), subjects, DOI/URL, and host-work details (book, volume, issue, pages).
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['reference_id'], 'properties': {'reference_id': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991}}}
get_semantic_map
Semantic scatter
A 2-D scatter of a filtered set, projected from the stored 768-dimension embeddings by PCA. Shows which items sit near each other in meaning — where a set splits into distinct strands and where it is one cloud. Read `explained_variance` before drawing any conclusion: with 768 dimensions the first two components usually carry a modest share, and a scatter explaining 6% of the variance is a much weaker claim than one explaining 40%. This is PCA, not UMAP: it spreads the broadest axes of variation and flattens fine cluster structure, so it is not comparable to the semantic landscapes on islam.zmo.de. Needs no API key — the vectors are a column in the dataset — but only items whose full text ships are embedded at all. NOTE the payload scales with `limit`: a point cloud is a chart, not something a text-only client can read, so for those the useful part is the explained-variance summary rather than the coordinates. Keep `limit` low unless a chart is going to be drawn.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'properties': {'limit': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991, 'description': 'Items projected (default 300, max 2000)'}, 'subset': {'type': 'string', 'description': 'articles (default) | publications | references'}, 'country': {'type': 'string', 'description': "Exact country name: Benin | Burkina Faso | Côte d'Ivoire | Niger | Nigeria | Togo (accents optional)"}, 'date_to': {'type': 'string', 'description': 'YYYY-MM-DD (or YYYY)'}, 'keyword': {'type': 'string', 'description': "ONE French concept keyword; substring over the subset's text fields"}, 'subject': {'type': 'string', 'description': 'Exact subject tag (pipe-aware)'}, 'color_by': {'type': 'string', 'description': "country | newspaper | subject | lda_topic_label | polarity (gpt-5-6-luna's label)"}, 'date_from': {'type': 'string', 'description': 'YYYY-MM-DD (or YYYY)'}, 'newspaper': {'type': 'string', 'description': 'Newspaper (articles) or periodical/series title (publications)'}}}
출력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['view', 'subset', 'filters', 'total_matches', 'projected', 'explained_variance', 'note'], 'properties': {'note': {'type': 'string'}, 'view': {'type': 'string'}, 'groups': {'type': 'object', 'propertyNames': {'type': 'string'}, 'additionalProperties': {'type': 'number'}}, 'points': {'type': 'array', 'items': {'type': 'object', 'properties': {}, 'additionalProperties': {}}}, 'subset': {'type': 'string'}, 'filters': {'type': 'object', 'properties': {}, 'additionalProperties': {}}, 'color_by': {'type': 'string'}, 'projected': {'type': 'number'}, 'total_matches': {'type': 'number'}, 'explained_variance': {'type': 'array', 'items': {'type': 'number'}}}, 'additionalProperties': False}
get_sentiment_distribution
Aggregate AI sentiment
Aggregate AI polarity, centrality and subjectivity across a filter set. 5 models scored the corpus independently — gpt-5-6-luna, mistral-small-2603, deepseek-v4-flash-0731, gemma-4-31b-it, qwen3-8-27b — so model:"all" returns each one's distribution plus how often they AGREE. Treat disagreement as a fact about the judgement rather than noise: corpus-wide the panel is unanimous on polarity for only ~32% of articles, so in a set where the models split no single one's number should be quoted alone. All three scales are ordinal French labels; subjectivity is much the weakest and ships a caveat to quote with it. Articles were scored whether or not their full text ships, so these shares are not subject to the OCR coverage limit. The models do NOT all cover the same articles, so read each one's `coverage` before comparing counts: ~51 non-francophone articles are unscored by design, and qwen3-8-27b is 200 further short on articles peripheral to Islam.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'properties': {'model': {'type': 'string', 'description': 'gpt-5-6-luna | mistral-small-2603 | deepseek-v4-flash-0731 | gemma-4-31b-it | qwen3-8-27b | all | consensus â\x80\x94 default gpt-5-6-luna; "all" adds the cross-model agreement, "consensus" returns the panel\'s precomputed majority (no annotator produced it, so it is never attributed to a model). The vendor shorthands chatgpt/mistral/deepseek/gemma/qwen also resolve to the model that ran. The generation-1 models (gemini-3-flash-preview, gpt-5-mini, ministral-14b-2512) are no longer served and return an error rather than a substitute â\x80\x94 and \'gemini\' is refused rather than read as gemma-4-31b-it, which is a different model line.'}, 'country': {'type': 'string', 'description': "Exact country name: Benin | Burkina Faso | Côte d'Ivoire | Niger | Togo (accents optional)"}, 'subject': {'type': 'string'}, 'newspaper': {'type': 'string'}}}
출력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['view', 'model', 'total_articles', 'filters'], 'properties': {'note': {'type': 'string'}, 'view': {'type': 'string'}, 'model': {'type': 'string'}, 'models': {'type': 'array', 'items': {'type': 'string'}}, 'filters': {'type': 'object', 'properties': {}, 'additionalProperties': {}}, 'by_model': {'type': 'object', 'propertyNames': {'type': 'string'}, 'additionalProperties': {'type': 'object', 'properties': {}, 'additionalProperties': {}}}, 'coverage': {'type': 'object', 'propertyNames': {'type': 'string'}, 'additionalProperties': {'type': 'number'}}, 'disputed': {'type': 'object', 'properties': {}, 'additionalProperties': {}}, 'agreement': {'type': 'object', 'properties': {}, 'additionalProperties': {}}, 'consensus': {'type': 'object', 'properties': {}, 'additionalProperties': {}}, 'model_caveat': {'type': 'string'}, 'subjectivity': {'type': 'object', 'properties': {}, 'additionalProperties': {}}, 'total_articles': {'type': 'number'}, 'agreement_matrix': {'type': 'object', 'properties': {}, 'additionalProperties': {}}, 'polarity_distribution': {'type': 'object', 'propertyNames': {'type': 'string'}, 'additionalProperties': {'type': 'number'}}, 'centrality_distribution': {'type': 'object', 'propertyNames': {'type': 'string'}, 'additionalProperties': {'type': 'number'}}, 'subjectivity_median_rank': {'type': 'object', 'properties': {}, 'additionalProperties': {}}}, 'additionalProperties': False}
get_similar_items
Find similar items
The items nearest to a given one in meaning, by cosine similarity over the stored embeddings. Answers 'what else is like this' without a keyword — it finds pieces on the same event or theme that share no vocabulary. A neighbour above ~0.85 is usually the same story reprinted or lightly rewritten, which is how to spot syndication in this corpus; 0.6-0.8 is 'same subject, different piece'. Needs no API key: the item's own vector is a column, so nothing has to be embedded at request time. This is per-item, NOT the corpus-wide near-duplicate sweep — that is an all-pairs job and belongs offline.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['id'], 'properties': {'id': {'type': 'string', 'description': "Item id â\x80\x94 either a bare o:id ('3064') or the namespaced form search returns ('articles:3064')"}, 'limit': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991, 'description': 'Neighbours returned (default 12, max 50)'}, 'subset': {'type': 'string', 'description': 'articles (default) | publications | references'}, 'min_score': {'type': 'number', 'description': 'Drop neighbours below this cosine similarity (0-1)'}}}
출력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['view', 'subset', 'source', 'neighbours', 'note'], 'properties': {'note': {'type': 'string'}, 'view': {'type': 'string'}, 'source': {'type': 'object', 'properties': {}, 'additionalProperties': {}}, 'subset': {'type': 'string'}, 'neighbours': {'type': 'array', 'items': {'type': 'object', 'properties': {}, 'additionalProperties': {}}}}, 'additionalProperties': False}
get_temporal_distribution
Coverage over time
Counts of matching items per year (or month) — the direct way to chart coverage trends over time instead of paging through search results. Defaults to articles; also works on publications, references, documents, audiovisual, and images. Accepts the same filters as the corresponding search_* tool (keyword = ONE substring over the subset's text fields, country, newspaper/series, subject, date range). Optional group_by=country|newspaper returns one distribution per group. Items dated only to a year keep a bare-year key even at month granularity; undated items are counted in undated_count, never dropped silently. Set calendar=hijri to bucket by the Islamic (Umm al-Qura) calendar instead — with granularity=lunar_month this collapses every year into the twelve lunar months, which is the ONLY way to see observance-driven coverage (Ramadan, Dhu al-Hijja/hajj, Shawwal/Korité): the lunar year drifts ~11 days against the Gregorian, so a Gregorian axis smears each observance across all twelve months. Hijri buckets need a full YYYY-MM-DD, so items dated only to a year or month are reported in imprecise_date_count.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'properties': {'subset': {'type': 'string', 'description': 'articles (default) | publications | references | documents | audiovisual'}, 'country': {'type': 'string', 'description': "Exact country name: Benin | Burkina Faso | Côte d'Ivoire | Niger | Nigeria | Togo (accents optional)"}, 'date_to': {'type': 'string', 'description': 'YYYY-MM-DD (or YYYY)'}, 'keyword': {'type': 'string', 'description': "ONE French concept keyword (French/English for references); substring over the subset's text fields"}, 'subject': {'type': 'string', 'description': 'Exact subject tag (pipe-aware)'}, 'calendar': {'type': 'string', 'description': 'gregorian (default) | hijri â\x80\x94 bucket by the Islamic (Umm al-Qura) calendar'}, 'group_by': {'type': 'string', 'description': 'country | newspaper â\x80\x94 one distribution per group value'}, 'date_from': {'type': 'string', 'description': 'YYYY-MM-DD (or YYYY)'}, 'newspaper': {'type': 'string', 'description': 'Newspaper (articles) or periodical/series title (publications)'}, 'granularity': {'type': 'string', 'description': 'year (default) | month | lunar_month (all years collapsed into 12 lunar months; needs calendar=hijri)'}}}
출력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['view', 'subset', 'granularity', 'filters', 'total_matches', 'dated_count', 'undated_count'], 'properties': {'note': {'type': 'string'}, 'view': {'type': 'string'}, 'subset': {'type': 'string'}, 'filters': {'type': 'object', 'properties': {}, 'additionalProperties': {}}, 'calendar': {'type': 'string'}, 'group_by': {'type': 'string'}, 'dated_count': {'type': 'number'}, 'granularity': {'type': 'string'}, 'distribution': {'type': 'object', 'propertyNames': {'type': 'string'}, 'additionalProperties': {'type': 'number'}}, 'month_labels': {'type': 'object', 'propertyNames': {'type': 'string'}, 'additionalProperties': {'type': 'string'}}, 'total_matches': {'type': 'number'}, 'undated_count': {'type': 'number'}, 'imprecise_date_count': {'type': 'number'}, 'distribution_by_group': {'type': 'object', 'propertyNames': {'type': 'string'}, 'additionalProperties': {'type': 'object', 'propertyNames': {'type': 'string'}, 'additionalProperties': {'type': 'number'}}}}, 'additionalProperties': False}
get_topic_distribution
Topic distribution
How a filtered set distributes across the precomputed LDA topics, each labelled by its top terms (articles carry 30 topics and are ~99.5% classified; references have their own 33-topic model and only ~46% carry an assignment, so read its `classified` against `total_matches`). Topics are assigned offline over the full text, so they describe what a piece is ABOUT rather than which words it contains — use this instead of keyword counting to map a corpus. Optional over_time returns per-year counts for the leading topics. min_prob keeps only articles where the topic is at least that dominant (mean assignment probability is 0.34, so 0.5 is already a strong filter).
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'properties': {'top_n': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991, 'description': 'Topics given their own band in over_time (default 8, max 15)'}, 'subset': {'type': 'string', 'description': 'articles (default) | references'}, 'country': {'type': 'string', 'description': "Exact country name: Benin | Burkina Faso | Côte d'Ivoire | Niger | Nigeria | Togo (accents optional)"}, 'date_to': {'type': 'string', 'description': 'YYYY-MM-DD (or YYYY)'}, 'keyword': {'type': 'string', 'description': "ONE French concept keyword; substring over the subset's text fields"}, 'subject': {'type': 'string', 'description': 'Exact subject tag (pipe-aware)'}, 'min_prob': {'type': 'number', 'description': '0-1; keep only assignments at or above this probability'}, 'date_from': {'type': 'string', 'description': 'YYYY-MM-DD (or YYYY)'}, 'newspaper': {'type': 'string', 'description': 'Newspaper (articles) or periodical/series title (publications)'}, 'over_time': {'type': 'boolean', 'description': 'Also return per-year counts for the leading topics'}}}
출력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['view', 'subset', 'filters', 'total_matches', 'classified', 'topics'], 'properties': {'note': {'type': 'string'}, 'span': {'type': 'array', 'items': {'type': 'string'}}, 'view': {'type': 'string'}, 'subset': {'type': 'string'}, 'topics': {'type': 'array', 'items': {'type': 'object', 'properties': {}, 'additionalProperties': {}}}, 'filters': {'type': 'object', 'properties': {}, 'additionalProperties': {}}, 'periods': {'type': 'array', 'items': {'type': 'string'}}, 'classified': {'type': 'number'}, 'total_matches': {'type': 'number'}, 'trend_by_topic': {'type': 'object', 'propertyNames': {'type': 'string'}, 'additionalProperties': {'type': 'object', 'properties': {}, 'additionalProperties': {}}}, 'series_by_topic': {'type': 'object', 'propertyNames': {'type': 'string'}, 'additionalProperties': {'type': 'object', 'propertyNames': {'type': 'string'}, 'additionalProperties': {'type': 'number'}}}}, 'additionalProperties': False}
list_audiovisual
List audiovisual materials
List audiovisual materials, newest first (francophone web video from Burkina Faso, Togo and Benin; deposited Nigerian Hausa/Arabic recordings). Filter by country, publishing channel or `source_type`.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'properties': {'limit': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991, 'description': 'Default 20, max 50'}, 'offset': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991}, 'country': {'type': 'string', 'description': "Exact country name: Benin | Burkina Faso | Côte d'Ivoire | Niger | Nigeria | Togo (accents optional). Burkina Faso, Togo, Benin and Nigeria only â\x80\x94 no Niger or Ivorian items"}, 'publisher': {'type': 'string', 'description': 'Substring on the publishing channel/broadcaster, e.g. RTB | AEEM | CERFI'}, 'source_type': {'type': 'string', 'description': 'youtube (harvested web video, the large majority) | deposited (recordings with a file, 47)'}}}
list_locations
List lieux from the index
List lieux from the IWAC index, sorted by frequency (most-referenced first). The optional 'country' filter selects entries that APPEAR IN records from that country (mentioned-in, not located-in), ranked by collection-wide 'frequency' — so foreign and cross-border entries can appear. Nigeria returns none here (index frequency is computed from articles + publications + references, which have no Nigerian items — Nigeria is audiovisual only).
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'properties': {'limit': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991, 'description': 'Default 50, max 200'}, 'offset': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991}, 'country': {'type': 'string', 'description': "Exact country name: Benin | Burkina Faso | Côte d'Ivoire | Niger | Nigeria | Togo (accents optional). Selects lieux MENTIONED IN records from that country, not entities located there"}}}
list_periodicals
List periodicals
List the Islamic periodical/series titles in the publications subset, with issue counts and year ranges. Use the returned newspaper value as the `newspaper` filter on search_publications.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'properties': {'country': {'type': 'string', 'description': "Exact country name: Benin | Burkina Faso | Côte d'Ivoire | Niger | Togo (accents optional)"}}}
출력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['view', 'total_periodicals', 'periodicals'], 'properties': {'view': {'type': 'string'}, 'periodicals': {'type': 'array', 'items': {'type': 'object', 'properties': {}, 'additionalProperties': {}}}, 'country_filter': {'type': 'string'}, 'total_periodicals': {'type': 'number'}}, 'additionalProperties': False}
list_persons
List personnes from the index
List personnes from the IWAC index, sorted by frequency (most-referenced first). The optional 'country' filter selects entries that APPEAR IN records from that country (mentioned-in, not located-in), ranked by collection-wide 'frequency' — so foreign and cross-border entries can appear. Nigeria returns none here (index frequency is computed from articles + publications + references, which have no Nigerian items — Nigeria is audiovisual only).
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'properties': {'limit': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991, 'description': 'Default 50, max 200'}, 'offset': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991}, 'country': {'type': 'string', 'description': "Exact country name: Benin | Burkina Faso | Côte d'Ivoire | Niger | Nigeria | Togo (accents optional). Selects personnes MENTIONED IN records from that country, not entities located there"}}}
list_subjects
List sujets from the index
List sujets from the IWAC index, sorted by frequency (most-referenced first).
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'properties': {'limit': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991, 'description': 'Default 50, max 200'}, 'offset': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991}}}
search
Search IWAC
Search the Islam West Africa Collection across newspaper articles, Islamic publications, archival documents, academic references, audiovisual recordings, photographs, and the authority index (persons/places/organisations/events/subjects). Pass ONE concept or name — e.g. 'Tijaniyya', 'laïcité', 'Sheikh Gumi', 'pèlerinage'. Matching is accent- and case-insensitive; a multi-word query requires every word to appear somewhere in the item, so prefer a single concept per call. Write query strings and concept keywords in French for press/publication/document/index discovery even when the user's report language is not French. Academic references are multilingual, so try French and English title/abstract terms when relevant; metadata/filter labels remain French. Use the French transliteration of Islamic terms (Tabaski not 'Eid al-Adha', charia not 'sharia', Maouloud not 'Mawlid'). Returns {results:[{id,title,url,category}], ranking}; each result's `category` names its subset and the `ranking` field documents the ordering. Pass an id to `fetch` to read the full text. For filtered queries (by country, date, or newspaper) use the search_* tools instead.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['query'], 'properties': {'limit': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991, 'description': 'Max results across all categories. Default 20, max 50.'}, 'query': {'type': 'string', 'minLength': 1, 'description': 'One concept, name, or short phrase; use French concept terms for primary sources, and French/English terms for references'}}}
출력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['results', 'count', 'limit', 'ranking', 'deep_scan'], 'properties': {'count': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991}, 'limit': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991}, 'ranking': {'type': 'string'}, 'results': {'type': 'array', 'items': {'type': 'object', 'required': ['id', 'category'], 'properties': {'id': {'type': 'string'}, 'url': {'type': 'string'}, 'title': {'type': 'string'}, 'category': {'type': 'string'}}, 'additionalProperties': {}}}, 'deep_scan': {'type': 'boolean'}, 'limit_warning': {'type': 'string'}, 'requested_limit': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991}, 'coverage_warning': {'type': 'string'}, 'unavailable_categories': {'type': 'array', 'items': {'type': 'string'}}}, 'additionalProperties': False}
search_articles
Search newspaper articles
Search IWAC newspaper articles by keyword (title + OCR + AI abstracts, French and English), country, newspaper, subject, and date range. Use French concept keywords regardless of the user's report language. Matching is accent- and case-insensitive.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'properties': {'limit': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991, 'description': 'Default 20, max 100 (10 and 25 with with_description)'}, 'offset': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991}, 'country': {'type': 'string', 'description': "Exact country name: Benin | Burkina Faso | Côte d'Ivoire | Niger | Togo (accents optional)"}, 'date_to': {'type': 'string', 'description': 'YYYY-MM-DD (or YYYY)'}, 'keyword': {'type': 'string', 'description': 'Concept keyword; substring match on title, OCR text, and the French and English AI abstracts. Prefer French for the OCR; an English term still matches via the English abstract'}, 'subject': {'type': 'string'}, 'date_from': {'type': 'string', 'description': 'YYYY-MM-DD (or YYYY)'}, 'newspaper': {'type': 'string'}, 'hijri_year': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991, 'description': 'Islamic (Umm al-Qura) year, e.g. 1445'}, 'hijri_month': {'type': 'string', 'description': 'Islamic lunar month: 1-12, or a name (Ramadan, Chaabane, Chawwal, Dhu al-Hijja). Pulls the articles behind an observance peak â\x80\x94 matches only items with a full YYYY-MM-DD date.'}, 'with_description': {'type': 'boolean', 'description': "Include each article's ~500-char AI abstract (description_ai) for triage without get_article. Adds ~125 tokens/row, so `limit` defaults to 10 and caps at 25 while this is on."}}}
search_audiovisual
Search audiovisual materials
Search audiovisual materials by keyword and metadata: francophone web video from Burkina Faso, Togo and Benin (TV reports, association and campus recordings), plus deposited Nigerian Hausa/Arabic recordings. Keyword matches title, creator, publisher, subject, spatial, language, source, the item's own description (the richest text most of these items have) and its transcription where one exists. Each row says which population it is from (`source_type`) and carries either `external_url` (a video to watch) or `media_url` (a file), never both.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'properties': {'limit': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991, 'description': 'Default 20, max 50'}, 'medium': {'type': 'string', 'description': 'Exact carrier medium: Vidéo sur le web | DVD | CD (validated, accents optional)'}, 'offset': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991}, 'country': {'type': 'string', 'description': "Exact country name: Benin | Burkina Faso | Côte d'Ivoire | Niger | Nigeria | Togo (accents optional). Burkina Faso, Togo, Benin and Nigeria only â\x80\x94 no Niger or Ivorian items"}, 'keyword': {'type': 'string', 'description': 'Substring match across audiovisual title/metadata fields'}, 'subject': {'type': 'string', 'description': 'Exact subject tag â\x80\x94 only ~27 rows carry one, so prefer publisher/keyword'}, 'language': {'type': 'string', 'description': 'Exact language value, e.g. Français | Haoussa | Arabe | Anglais | Mooré'}, 'publisher': {'type': 'string', 'description': 'Substring on the publishing channel/broadcaster, e.g. RTB | AEEM | CERFI'}, 'source_type': {'type': 'string', 'description': 'youtube (harvested web video, the large majority) | deposited (recordings with a file, 47)'}}}
search_by_sentiment
Filter articles by AI sentiment
Filter articles by gpt-5-6-luna sentiment labels (accent/case-insensitive exact match). One model's reading, not a consensus — 4 other models scored the same articles and often disagree; get_sentiment_distribution with model:"all" shows by how much. `subjectivity` is much the weakest of the three scales, so treat a set selected on it as a lead to read rather than as a finding.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'properties': {'limit': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991, 'description': 'Default 20, max 100'}, 'offset': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991}, 'country': {'type': 'string', 'description': "Exact country name: Benin | Burkina Faso | Côte d'Ivoire | Niger | Togo (accents optional)"}, 'subject': {'type': 'string'}, 'disputed': {'type': 'string', 'description': 'polarite | centralite | subjectivite â\x80\x94 keep only articles the panel SPLIT on for that field (French field names, as stored). Selects contested readings, not a sentiment value.'}, 'polarity': {'type': 'string', 'description': 'Très positif | Positif | Neutre | Négatif | Très négatif | Non applicable'}, 'centrality': {'type': 'string', 'description': 'Très central | Central | Secondaire | Marginal | Non abordé'}, 'subjectivity': {'type': 'string', 'description': 'Très objectif | Plutôt objectif | Mixte | Plutôt subjectif | Très subjectif â\x80\x94 least to most subjective. Unscored where the model answered Non abordé, so this filter also excludes those.'}}}
search_documents
Search archival documents
Search the small archival-documents subset (~26 items: Islamic association reports, flyers, project documents — mostly Burkina Faso). Use French concept keywords regardless of the user's report language. Most have OCR text and an AI description. Call with no arguments to list all.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'properties': {'limit': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991, 'description': 'Default 15, max 50'}, 'offset': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991}, 'country': {'type': 'string', 'description': "Exact country name: Benin | Burkina Faso | Côte d'Ivoire | Niger | Nigeria | Togo (accents optional). Corpus is mostly Burkina Faso/Togo/Benin"}, 'keyword': {'type': 'string', 'description': 'Concept keyword; substring match on title, OCR, the French and English AI descriptions, and subject (accent-insensitive)'}}}
search_images
Search photographs
Search the IWAC photographs (30 items: mosques, radio stations, schools, signage and street scenes documented during fieldwork). Keyword matches title, creator, subject, place and the rare caption. Each result carries `image_url` (the full-resolution file), `coordinates` ('lat, lng' where known) and the canonical IWAC page. Call with no arguments to list all. Captions are almost never present, so prefer subject/place filters over keywords, or semantic_search_images when it is enabled.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'properties': {'limit': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991, 'description': 'Default 20, max 50'}, 'offset': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991}, 'country': {'type': 'string', 'description': "Exact country name: Benin | Burkina Faso | Côte d'Ivoire | Niger | Nigeria | Togo (accents optional)"}, 'creator': {'type': 'string', 'description': 'Photographer name (substring match)'}, 'date_to': {'type': 'string', 'description': 'YYYY-MM-DD (or YYYY)'}, 'keyword': {'type': 'string', 'description': 'French concept keyword; substring match on title, creator, subject, place and caption'}, 'spatial': {'type': 'string', 'description': 'Exact place name, e.g. Ouagadougou (pipe-aware)'}, 'subject': {'type': 'string', 'description': 'Exact subject tag (pipe-aware)'}, 'date_from': {'type': 'string', 'description': 'YYYY-MM-DD (or YYYY)'}}}
search_index
Search authority index
Search the IWAC authority index (persons, places, organisations, events, subjects) by name. Accent/case-insensitive.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'required': ['keyword'], 'properties': {'limit': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991, 'description': 'Default 20, max 100'}, 'offset': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991}, 'keyword': {'type': 'string', 'description': 'Search term matched against the entry title'}, 'index_type': {'type': 'string', 'description': "Exact type (accents optional), validated against: Personnes | Lieux | Organisations | Ã\x89vénements | Sujets | Notices d'autorité. An unrecognised value returns an error listing the valid types."}}}
search_publications
Search publications
Search Islamic publications (periodical issues, books). `keyword` matches title, subject, table of contents, and full OCR text (TOC hits come back as matching_toc_entries); use French concept keywords regardless of the user's report language. Filter by newspaper/series, subject, country and year. Use list_periodicals to discover series titles, and get_publication_fulltext for keyword excerpts from a single issue.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'properties': {'limit': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991, 'description': 'Default 20, max 100'}, 'offset': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991}, 'country': {'type': 'string', 'description': "Exact country name: Benin | Burkina Faso | Côte d'Ivoire | Niger | Togo (accents optional)"}, 'date_to': {'type': 'string', 'description': 'Latest year, YYYY'}, 'keyword': {'type': 'string', 'description': 'French concept keyword; substring match on title + subject + table of contents + OCR (accent-insensitive)'}, 'subject': {'type': 'string', 'description': 'Subject tag (~87% of issues are tagged)'}, 'date_from': {'type': 'string', 'description': 'Earliest year, YYYY'}, 'newspaper': {'type': 'string', 'description': 'Periodical/series title (see list_periodicals)'}, 'hijri_year': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991, 'description': 'Islamic (Umm al-Qura) year, e.g. 1445'}, 'hijri_month': {'type': 'string', 'description': 'Islamic lunar month: 1-12, or a name (Ramadan, Chaabane, Chawwal, Dhu al-Hijja). Matches only issues with a full YYYY-MM-DD date â\x80\x94 ~83% of them.'}}}
search_references
Search academic references
Search academic references (journal articles, book chapters, theses, books, reports) by keyword and metadata. `keyword` is a single substring match over title + abstract, so search ONE term per call (combined terms like 'pèlerinage Mecque' miss results). References are multilingual: try French and English title/abstract keywords when relevant; metadata/filter values such as `reference_type` and `language` use French labels. Results include a short abstract snippet — use get_reference for the full abstract and bibliographic detail.
읽기 전용 멱등성
입력 스키마 
{'type': 'object', '$schema': 'https://json-schema.org/draft/2020-12/schema', 'properties': {'limit': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991, 'description': 'Default 20, max 100'}, 'author': {'type': 'string'}, 'offset': {'type': 'integer', 'maximum': 9007199254740991, 'minimum': -9007199254740991}, 'country': {'type': 'string', 'description': "Exact country name: Benin | Burkina Faso | Côte d'Ivoire | Niger | Nigeria | Togo (accents optional)"}, 'date_to': {'type': 'string', 'description': 'Latest year, YYYY'}, 'keyword': {'type': 'string', 'description': 'One French or English concept keyword; substring match on title + abstract (one term per call, accent-insensitive)'}, 'subject': {'type': 'string', 'description': 'Subject tag (sparse: ~27% of references are tagged)'}, 'language': {'type': 'string', 'description': 'e.g. Français | Anglais'}, 'date_from': {'type': 'string', 'description': 'Earliest year, YYYY'}, 'reference_type': {'type': 'string', 'description': "Substring match. Values: Article de revue | Chapitre de livre | Livre | Mémoire de maitrise | Rapport | Thèse de doctorat | Communication scientifique | Compte rendu de livre | Article d'encyclopédie | Mémoire de licence | Article de blog | Working paper. Use the full label for precision â\x80\x94 'Livre' alone also matches 'Chapitre de livre' and 'Compte rendu de livre'."}}}
기록

최근 도구 변경

추가됨
get_image
2026년 9월 17일 12:41 PM
추가됨
search_images
2026년 9월 17일 12:41 PM
추가됨
get_audiovisual
2026년 9월 17일 12:41 PM
추가됨
list_audiovisual
2026년 9월 17일 12:41 PM
추가됨
search_audiovisual
2026년 9월 17일 12:41 PM
추가됨
get_document
2026년 9월 17일 12:41 PM
추가됨
search_documents
2026년 9월 17일 12:41 PM
추가됨
get_reference
2026년 9월 17일 12:41 PM
추가됨
search_references
2026년 9월 17일 12:41 PM
추가됨
get_publication_fulltext
2026년 9월 17일 12:41 PM
추가됨
list_periodicals
2026년 9월 17일 12:41 PM
추가됨
search_publications
2026년 9월 17일 12:41 PM
추가됨
get_lexical_metrics
2026년 9월 17일 12:41 PM
추가됨
get_similar_items
2026년 9월 17일 12:41 PM
추가됨
get_semantic_map
2026년 9월 17일 12:41 PM
추가됨
get_place_distribution
2026년 9월 17일 12:41 PM
추가됨
get_cooccurrence
2026년 9월 17일 12:41 PM
추가됨
get_field_distribution
2026년 9월 17일 12:41 PM
추가됨
get_topic_distribution
2026년 9월 17일 12:41 PM
추가됨
get_temporal_distribution
2026년 9월 17일 12:41 PM
추가됨
get_country_comparison
2026년 9월 17일 12:41 PM
추가됨
get_newspaper_stats
2026년 9월 17일 12:41 PM
추가됨
get_collection_stats
2026년 9월 17일 12:41 PM
추가됨
list_persons
2026년 9월 17일 12:41 PM
추가됨
list_locations
2026년 9월 17일 12:41 PM
추가됨
list_subjects
2026년 9월 17일 12:41 PM
추가됨
get_index_entry
2026년 9월 17일 12:41 PM
추가됨
search_index
2026년 9월 17일 12:41 PM
추가됨
get_sentiment_distribution
2026년 9월 17일 12:41 PM
추가됨
search_by_sentiment
2026년 9월 17일 12:41 PM
탐색

유사한 MCP 서버

Data Govt Nz

io.github.pipeworx-io/data-govt-nz

Searches and retrieves metadata for datasets, organizations, groups, and resources in New Zealand’s data.govt.nz CKAN open-data c…

Figshare

io.github.pipeworx-io/figshare

Searches and retrieves research outputs from Figshare, including papers, datasets, posters, code, metadata, files, collections, a…

NicheDB

dev.nichedb/nichedb

Aggregates scheduled public-data sources into searchable collections, enriched items, profiles, feeds, and future-event listings.

ColdState — Deterministic Knowledge Infrastructure

ai.coldstate/knowledge

Searches, retrieves, cites and verifies entries in a deterministic multi-domain knowledge base.

wikidata-mcp-server

io.github.cyanheads/wikidata-mcp-server

Searches and retrieves Wikidata entities, resolves external identifiers, and runs SPARQL queries over the Wikidata graph.

LiveDataLink

io.github.blackboxfoundry/livedatalink

Exposes public-data tools covering economics, labor, banking, transport, books, legal opinions, carriers, environmental data, and…

Stratalize Healthcare

com.stratalize/healthcare

Retired Stratalize server exposing benchmark, market intelligence, compliance, financial, healthcare, legal, real estate, and eco…

Stratalize Governance

com.stratalize/governance

Retired Stratalize server exposing benchmark, market intelligence, compliance, financial, healthcare, legal, real estate, and eco…