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MCP 服务器

paper-mcp

io.github.MCPServings/paper-mcp
科学与工程 搜索与研究 公开且可连接 MCP 2025-11-25

此 MCP 可以做什么

Searches and retrieves academic papers and citation data across arXiv, Semantic Scholar, OpenAlex, PubMed, and Europe PMC, with PDF extraction, OCR, and LaTeX tools.

功能

工具

41 工具
autocomplete_papers
Semantic Scholar: autocomplete paper titles for a partial query (fast type-ahead).
输入模式 
{'type': 'object', 'title': 'autocomplete_papersArguments', 'required': ['query'], 'properties': {'query': {'type': 'string', 'title': 'Query'}}}
extract_pdf
Extract a PDF to clean Markdown/LaTeX text via MinerU (great for papers behind no open-access full text — give the user's PDF and get readable text back). Provide pdf_url (downloaded server-side, SSRF-guarded) OR pdf_base64. formula/table toggle math/table reconstruction. Returns {task_id, status, cached, content, chars}: a recently-seen (cached) or small PDF comes back with `content` in one call; a fresh PDF (MinerU is GPU-heavy, minutes) returns status='running' + a task_id — then call extract_pdf_result(task_id) to fetch the text.
输入模式 
{'type': 'object', 'title': 'extract_pdfArguments', 'properties': {'table': {'type': 'boolean', 'title': 'Table', 'default': True}, 'formula': {'type': 'boolean', 'title': 'Formula', 'default': True}, 'pdf_url': {'type': 'string', 'title': 'Pdf Url', 'default': ''}, 'pdf_base64': {'type': 'string', 'title': 'Pdf Base64', 'default': ''}}}
extract_pdf_result
Fetch the result of an extract_pdf job by task_id. Returns {task_id, status, content, chars}: `content` is the extracted text once status='done'; while still 'running' content is null — call again shortly. Results expire server-side, so fetch reasonably soon.
输入模式 
{'type': 'object', 'title': 'extract_pdf_resultArguments', 'required': ['task_id'], 'properties': {'task_id': {'type': 'string', 'title': 'Task Id'}}}
get_author
Semantic Scholar: a single author's profile by id.
输入模式 
{'type': 'object', 'title': 'get_authorArguments', 'required': ['author_id'], 'properties': {'author_id': {'type': 'string', 'title': 'Author Id'}}}
get_author_papers
Semantic Scholar: all papers by a given author id, newest first.
输入模式 
{'type': 'object', 'title': 'get_author_papersArguments', 'required': ['author_id'], 'properties': {'start': {'type': 'integer', 'title': 'Start', 'default': 0}, 'author_id': {'type': 'string', 'title': 'Author Id'}, 'max_results': {'type': 'integer', 'title': 'Max Results', 'default': 20}}}
get_authors_batch
Semantic Scholar: fetch many authors at once by id.
输入模式 
{'type': 'object', 'title': 'get_authors_batchArguments', 'required': ['ids'], 'properties': {'ids': {'type': 'array', 'items': {'type': 'string'}, 'title': 'Ids'}}}
get_dataset_diffs
Semantic Scholar Datasets: incremental diff (added/updated/deleted) for a dataset between two releases. Needs the key.
输入模式 
{'type': 'object', 'title': 'get_dataset_diffsArguments', 'required': ['dataset_name', 'start_release'], 'properties': {'end_release': {'type': 'string', 'title': 'End Release', 'default': 'latest'}, 'dataset_name': {'type': 'string', 'title': 'Dataset Name'}, 'start_release': {'type': 'string', 'title': 'Start Release'}}}
get_dataset_download_links
Semantic Scholar Datasets: get download links (presigned URLs) for one dataset in a release. Needs the API key.
输入模式 
{'type': 'object', 'title': 'get_dataset_download_linksArguments', 'required': ['dataset_name'], 'properties': {'release_id': {'type': 'string', 'title': 'Release Id', 'default': 'latest'}, 'dataset_name': {'type': 'string', 'title': 'Dataset Name'}}}
get_dataset_release
Semantic Scholar Datasets: which datasets a release contains (papers, abstracts, citations, embeddings, s2orc, tldrs…). release_id defaults to 'latest'.
输入模式 
{'type': 'object', 'title': 'get_dataset_releaseArguments', 'properties': {'release_id': {'type': 'string', 'title': 'Release Id', 'default': 'latest'}}}
get_openalex_citations
OpenAlex: papers that CITE this work (forward citation graph), most-cited first.
输入模式 
{'type': 'object', 'title': 'get_openalex_citationsArguments', 'required': ['work_id'], 'properties': {'start': {'type': 'integer', 'title': 'Start', 'default': 0}, 'work_id': {'type': 'string', 'title': 'Work Id'}, 'max_results': {'type': 'integer', 'title': 'Max Results', 'default': 10}}}
get_openalex_references
OpenAlex: the works this one REFERENCES (its bibliography).
输入模式 
{'type': 'object', 'title': 'get_openalex_referencesArguments', 'required': ['work_id'], 'properties': {'work_id': {'type': 'string', 'title': 'Work Id'}, 'max_results': {'type': 'integer', 'title': 'Max Results', 'default': 25}}}
get_openalex_trends
OpenAlex: publication-trend analytics for a query — counts grouped by year (default), or by 'institutions.id', 'authorships.author.id', 'open_access.is_oa', 'type', 'language'. Returns aggregate counts only (cheap, no rows).
输入模式 
{'type': 'object', 'title': 'get_openalex_trendsArguments', 'required': ['query'], 'properties': {'query': {'type': 'string', 'title': 'Query'}, 'group_by': {'type': 'string', 'title': 'Group By', 'default': 'publication_year'}}}
get_openalex_work
OpenAlex: fetch one work's full record (316M-work, all-field corpus). id accepts OpenAlex Wxxxx, a DOI, or an arXiv id.
输入模式 
{'type': 'object', 'title': 'get_openalex_workArguments', 'required': ['work_id'], 'properties': {'work_id': {'type': 'string', 'title': 'Work Id'}}}
get_paper
Fetch one paper by id, with full abstract and PDF link.
输入模式 
{'type': 'object', 'title': 'get_paperArguments', 'required': ['paper_id'], 'properties': {'source': {'type': 'string', 'title': 'Source', 'default': 'arxiv'}, 'paper_id': {'type': 'string', 'title': 'Paper Id'}}}
get_paper_authors
Semantic Scholar: the authors of a paper (with h-index, paper/citation counts).
输入模式 
{'type': 'object', 'title': 'get_paper_authorsArguments', 'required': ['paper_id'], 'properties': {'start': {'type': 'integer', 'title': 'Start', 'default': 0}, 'paper_id': {'type': 'string', 'title': 'Paper Id'}, 'max_results': {'type': 'integer', 'title': 'Max Results', 'default': 100}}}
get_paper_citations
Semantic Scholar: papers that CITE this one (forward citation graph). id accepts S2 id / DOI: / ARXIV: / CorpusId:.
输入模式 
{'type': 'object', 'title': 'get_paper_citationsArguments', 'required': ['paper_id'], 'properties': {'start': {'type': 'integer', 'title': 'Start', 'default': 0}, 'paper_id': {'type': 'string', 'title': 'Paper Id'}, 'max_results': {'type': 'integer', 'title': 'Max Results', 'default': 10}}}
get_paper_references
Semantic Scholar: papers this one REFERENCES (its bibliography). id accepts S2 id / DOI: / ARXIV: / CorpusId:.
输入模式 
{'type': 'object', 'title': 'get_paper_referencesArguments', 'required': ['paper_id'], 'properties': {'start': {'type': 'integer', 'title': 'Start', 'default': 0}, 'paper_id': {'type': 'string', 'title': 'Paper Id'}, 'max_results': {'type': 'integer', 'title': 'Max Results', 'default': 10}}}
get_papers_batch
Semantic Scholar: fetch many papers at once by id (S2/DOI:/ARXIV:/CorpusId:), up to ~500 per call.
输入模式 
{'type': 'object', 'title': 'get_papers_batchArguments', 'required': ['ids'], 'properties': {'ids': {'type': 'array', 'items': {'type': 'string'}, 'title': 'Ids'}}}
lint_latex
Lint a LaTeX snippet: report errors and return an auto-fixed version. Input `code` (the LaTeX source). Returns {errors, fixed_code, summary_en, summary_zh, elapsed_ms}.
输入模式 
{'type': 'object', 'title': 'lint_latexArguments', 'required': ['code'], 'properties': {'code': {'type': 'string', 'title': 'Code'}}}
list_categories
List common subject category codes for filtering/recent.
输入模式 
{'type': 'object', 'title': 'list_categoriesArguments', 'properties': {'source': {'type': 'string', 'title': 'Source', 'default': 'arxiv'}}}
list_dataset_releases
Semantic Scholar Datasets: list all available release ids (dated snapshots of the full corpus).
输入模式 
{'type': 'object', 'title': 'list_dataset_releasesArguments', 'properties': {}}
list_ocr_models
List the OCR models available for recognize_formula / recognize_table.
输入模式 
{'type': 'object', 'title': 'list_ocr_modelsArguments', 'properties': {}}
list_openalex_topics
OpenAlex: search the topic taxonomy (~4500 topics) to find the right subject term for filtering or recent-work queries.
输入模式 
{'type': 'object', 'title': 'list_openalex_topicsArguments', 'required': ['query'], 'properties': {'query': {'type': 'string', 'title': 'Query'}, 'max_results': {'type': 'integer', 'title': 'Max Results', 'default': 15}}}
list_paper_sources
List available paper corpora.
输入模式 
{'type': 'object', 'title': 'list_paper_sourcesArguments', 'properties': {}}
list_recent
List the latest papers in a subject category, newest first.
输入模式 
{'type': 'object', 'title': 'list_recentArguments', 'required': ['category'], 'properties': {'start': {'type': 'integer', 'title': 'Start', 'default': 0}, 'source': {'type': 'string', 'title': 'Source', 'default': 'arxiv'}, 'category': {'type': 'string', 'title': 'Category'}, 'max_results': {'type': 'integer', 'title': 'Max Results', 'default': 10}}}
match_paper_title
Semantic Scholar: find the single paper whose title best matches the given text (exact-match lookup).
输入模式 
{'type': 'object', 'title': 'match_paper_titleArguments', 'required': ['title'], 'properties': {'title': {'type': 'string', 'title': 'Title'}}}
read_paper
Read a paper's full text. format='markdown' (default, body with formulas as $LaTeX$), 'html' (raw LaTeXML HTML), or 'latex' (the original LaTeX manuscript from the e-print source). arXiv only; id like 2401.01234.
输入模式 
{'type': 'object', 'title': 'read_paperArguments', 'required': ['paper_id'], 'properties': {'format': {'type': 'string', 'title': 'Format', 'default': 'markdown'}, 'source': {'type': 'string', 'title': 'Source', 'default': 'arxiv'}, 'paper_id': {'type': 'string', 'title': 'Paper Id'}}}
recognize_formula
Recognize a math formula from an image and return LaTeX. Provide image_url (downloaded server-side) OR image_base64. model: deepseek-ocr (default), paddleocr-vl, or texify. Returns {latex, model, elapsed_ms}.
输入模式 
{'type': 'object', 'title': 'recognize_formulaArguments', 'properties': {'model': {'type': 'string', 'title': 'Model', 'default': 'deepseek-ocr'}, 'image_url': {'type': 'string', 'title': 'Image Url', 'default': ''}, 'image_base64': {'type': 'string', 'title': 'Image Base64', 'default': ''}}}
recognize_table
Recognize a table from an image and return LaTeX tabular code. Provide image_url OR image_base64. model: deepseek-ocr (default), paddleocr-vl, or texify. Returns {latex, model, elapsed_ms}.
输入模式 
{'type': 'object', 'title': 'recognize_tableArguments', 'properties': {'model': {'type': 'string', 'title': 'Model', 'default': 'deepseek-ocr'}, 'image_url': {'type': 'string', 'title': 'Image Url', 'default': ''}, 'image_base64': {'type': 'string', 'title': 'Image Base64', 'default': ''}}}
recommend_papers_for_paper
Semantic Scholar: recommend papers similar to one paper. pool='recent' (last open corpus) or 'all-cs' (all of CS). If the 'recent' pool yields nothing (common for older papers), it automatically retries the 'all-cs' pool.
输入模式 
{'type': 'object', 'title': 'recommend_papers_for_paperArguments', 'required': ['paper_id'], 'properties': {'pool': {'type': 'string', 'title': 'Pool', 'default': 'recent'}, 'paper_id': {'type': 'string', 'title': 'Paper Id'}, 'max_results': {'type': 'integer', 'title': 'Max Results', 'default': 10}}}
recommend_papers_from_examples
Semantic Scholar: recommend papers from positive (and optional negative) example paper ids.
输入模式 
{'type': 'object', 'title': 'recommend_papers_from_examplesArguments', 'required': ['positive_ids'], 'properties': {'max_results': {'type': 'integer', 'title': 'Max Results', 'default': 10}, 'negative_ids': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'title': 'Negative Ids', 'default': None}, 'positive_ids': {'type': 'array', 'items': {'type': 'string'}, 'title': 'Positive Ids'}}}
search_all
Aggregated search across arXiv, Semantic Scholar and OpenAlex at once. Fans out concurrently, de-duplicates the same work across corpora (by DOI or title) and re-ranks with Reciprocal Rank Fusion, so papers found by several sources rank highest. Each hit lists which `sources` found it and an `ids` map ({source: id}) you can pass to get_paper / read_paper / the citation tools. Prefer this over search_papers for a broad lookup.
输入模式 
{'type': 'object', 'title': 'search_allArguments', 'required': ['query'], 'properties': {'query': {'type': 'string', 'title': 'Query'}, 'sources': {'type': 'string', 'title': 'Sources', 'default': 'arxiv,semanticscholar,openalex'}, 'per_source': {'type': 'integer', 'title': 'Per Source', 'default': 0}, 'max_results': {'type': 'integer', 'title': 'Max Results', 'default': 10}}}
search_authors
Semantic Scholar: search for authors by name; returns profiles with h-index and paper/citation counts.
输入模式 
{'type': 'object', 'title': 'search_authorsArguments', 'required': ['query'], 'properties': {'query': {'type': 'string', 'title': 'Query'}, 'start': {'type': 'integer', 'title': 'Start', 'default': 0}, 'max_results': {'type': 'integer', 'title': 'Max Results', 'default': 10}}}
search_by_author
Find papers by a specific author, newest first.
输入模式 
{'type': 'object', 'title': 'search_by_authorArguments', 'required': ['author'], 'properties': {'start': {'type': 'integer', 'title': 'Start', 'default': 0}, 'author': {'type': 'string', 'title': 'Author'}, 'source': {'type': 'string', 'title': 'Source', 'default': 'arxiv'}, 'max_results': {'type': 'integer', 'title': 'Max Results', 'default': 10}}}
search_medical
Evidence-graded MEDICAL literature search (PubMed + Europe PMC). Unlike search_all (generic, ranks high-cited reviews/guidelines above trials), this filters by research type via PubMed Publication-Type tags and re-ranks by the evidence pyramid (meta-analysis / systematic review > RCT > cohort > ...), so the actual clinical trials surface first. Open-access full text is pulled from Europe PMC by PMID. `query` should be English keyword/boolean text (PubMed maps it); do natural-language/multilingual understanding upstream. Returns hits with pmid/doi/study_type/evidence_level/citations/abstract and, when open-access, fulltext.
输入模式 
{'type': 'object', 'title': 'search_medicalArguments', 'required': ['query'], 'properties': {'query': {'type': 'string', 'title': 'Query'}, 'year_from': {'type': 'integer', 'title': 'Year From', 'default': 0}, 'max_results': {'type': 'integer', 'title': 'Max Results', 'default': 10}, 'study_types': {'type': 'string', 'title': 'Study Types', 'default': 'rct,meta-analysis,systematic-review'}, 'fetch_fulltext': {'type': 'boolean', 'title': 'Fetch Fulltext', 'default': True}}}
search_openalex_authors
OpenAlex: search authors; returns profiles with h-index, i10-index, works/citation counts and institutions.
输入模式 
{'type': 'object', 'title': 'search_openalex_authorsArguments', 'required': ['query'], 'properties': {'query': {'type': 'string', 'title': 'Query'}, 'start': {'type': 'integer', 'title': 'Start', 'default': 0}, 'max_results': {'type': 'integer', 'title': 'Max Results', 'default': 10}}}
search_openalex_institutions
OpenAlex: search institutions (universities, labs) with ROR id, country, works/citation counts.
输入模式 
{'type': 'object', 'title': 'search_openalex_institutionsArguments', 'required': ['query'], 'properties': {'query': {'type': 'string', 'title': 'Query'}, 'max_results': {'type': 'integer', 'title': 'Max Results', 'default': 10}}}
search_openalex_works
OpenAlex: advanced filtered work search. Filters: from_year, to_year, is_oa (open access only), min_citations, institution_id. sort_by: relevance|newest|cited.
输入模式 
{'type': 'object', 'title': 'search_openalex_worksArguments', 'properties': {'is_oa': {'type': 'boolean', 'title': 'Is Oa', 'default': False}, 'query': {'type': 'string', 'title': 'Query', 'default': ''}, 'sort_by': {'type': 'string', 'title': 'Sort By', 'default': 'relevance'}, 'to_year': {'type': 'integer', 'title': 'To Year', 'default': 0}, 'from_year': {'type': 'integer', 'title': 'From Year', 'default': 0}, 'max_results': {'type': 'integer', 'title': 'Max Results', 'default': 25}, 'min_citations': {'type': 'integer', 'title': 'Min Citations', 'default': 0}, 'institution_id': {'type': 'string', 'title': 'Institution Id', 'default': ''}}}
search_papers
Search academic papers. Returns normalized hits with a short abstract preview; call get_paper for the full record.
输入模式 
{'type': 'object', 'title': 'search_papersArguments', 'required': ['query'], 'properties': {'query': {'type': 'string', 'title': 'Query'}, 'start': {'type': 'integer', 'title': 'Start', 'default': 0}, 'source': {'type': 'string', 'title': 'Source', 'default': 'arxiv'}, 'sort_by': {'type': 'string', 'title': 'Sort By', 'default': 'relevance'}, 'max_results': {'type': 'integer', 'title': 'Max Results', 'default': 10}}}
search_papers_bulk
Semantic Scholar: bulk paper search (up to 1000 hits, sortable e.g. 'citationCount:desc' or 'publicationDate:desc', with a continuation token). Filters: fields_of_study, year (e.g. '2020-2024'), venue, publication_types, open_access_pdf.
输入模式 
{'type': 'object', 'title': 'search_papers_bulkArguments', 'required': ['query'], 'properties': {'sort': {'type': 'string', 'title': 'Sort', 'default': ''}, 'year': {'type': 'string', 'title': 'Year', 'default': ''}, 'query': {'type': 'string', 'title': 'Query'}, 'token': {'type': 'string', 'title': 'Token', 'default': ''}, 'venue': {'type': 'string', 'title': 'Venue', 'default': ''}, 'max_results': {'type': 'integer', 'title': 'Max Results', 'default': 100}, 'fields_of_study': {'type': 'string', 'title': 'Fields Of Study', 'default': ''}, 'open_access_pdf': {'type': 'boolean', 'title': 'Open Access Pdf', 'default': False}, 'publication_types': {'type': 'string', 'title': 'Publication Types', 'default': ''}}}
search_snippets
Semantic Scholar: search INSIDE paper full text and return matching text snippets (not just titles/abstracts).
输入模式 
{'type': 'object', 'title': 'search_snippetsArguments', 'required': ['query'], 'properties': {'query': {'type': 'string', 'title': 'Query'}, 'max_results': {'type': 'integer', 'title': 'Max Results', 'default': 10}}}
历史

近期工具变更

已添加
list_openalex_topics
2026年9月17日 12:45
已添加
get_openalex_trends
2026年9月17日 12:45
已添加
search_openalex_works
2026年9月17日 12:45
已添加
search_openalex_institutions
2026年9月17日 12:45
已添加
search_openalex_authors
2026年9月17日 12:45
已添加
get_openalex_references
2026年9月17日 12:45
已添加
get_openalex_citations
2026年9月17日 12:45
已添加
get_openalex_work
2026年9月17日 12:45
已添加
get_dataset_diffs
2026年9月17日 12:45
已添加
get_dataset_download_links
2026年9月17日 12:45
已添加
get_dataset_release
2026年9月17日 12:45
已添加
list_dataset_releases
2026年9月17日 12:45
已添加
recommend_papers_from_examples
2026年9月17日 12:45
已添加
recommend_papers_for_paper
2026年9月17日 12:45
已添加
search_snippets
2026年9月17日 12:45
已添加
get_authors_batch
2026年9月17日 12:45
已添加
get_author_papers
2026年9月17日 12:45
已添加
get_author
2026年9月17日 12:45
已添加
search_authors
2026年9月17日 12:45
已添加
get_papers_batch
2026年9月17日 12:45
已添加
search_papers_bulk
2026年9月17日 12:45
已添加
autocomplete_papers
2026年9月17日 12:45
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match_paper_title
2026年9月17日 12:45
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get_paper_authors
2026年9月17日 12:45
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get_paper_references
2026年9月17日 12:45
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get_paper_citations
2026年9月17日 12:45
已添加
extract_pdf_result
2026年9月17日 12:45
已添加
extract_pdf
2026年9月17日 12:45
已添加
lint_latex
2026年9月17日 12:45
已添加
list_ocr_models
2026年9月17日 12:45
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