此 MCP 可以做什么
Creates styled choropleth and categorical maps of India from CSV or Excel data.
功能
工具
5
工具
geocode
Geocode addresses from an uploaded CSV or XLSX file into latitude/longitude. Pass a file_key from upload_file.
Column selection: if you know it, pass address_column (or address_columns for several). Otherwise the server asks the geo-match service to detect it. When the answer is not unambiguous â several address columns, a weak match, or only structured columns like District/State/PIN that must be combined â the response is status=clarification_required with candidates and ready-to-fire guidance actions. Present the choice to the user and re-call with their answer; never invent a column name.
The result overwrites the same file_key as a new version, plus a small preview, not the full rows. Pass that file_key to question_to_map to map the coordinates, or hand the user file_url (a signed download link, when present) to fetch the file directly.
输入模式
{'type': 'object', 'properties': {'sheet': {'type': 'string'}, 'job_id': {'type': 'string'}, 'file_key': {'type': 'string'}, 'force_new': {'type': 'boolean'}, 'address_column': {'type': 'string'}, 'address_columns': {'type': ['null', 'array'], 'items': {'type': 'string'}}, 'compose_columns': {'type': ['null', 'array'], 'items': {'type': 'string'}}}, 'additionalProperties': False}
get_capabilities
Return the server's capability matrix: supported map types, geo types, output formats, classification methods, and the list of tools available in this process (which varies by configuration). Call this first to discover what the server can do.
输入模式
{'type': 'object', 'additionalProperties': False}
输出模式
{'type': 'object', 'required': ['map_types', 'geo_levels', 'output_formats', 'default_output', 'classification_methods', 'available_indices', 'index_modes', 'geo_detection', 'auto_level_detection', 'svg_default', 'png_supported', 'stateless_only', 'available_tools'], 'properties': {'guidance': {'type': ['null', 'object'], 'required': ['next_actions', 'allow_freeform', 'next_steps_text'], 'properties': {'next_actions': {'type': ['null', 'array'], 'items': {'type': 'object', 'required': ['id', 'label', 'tool'], 'properties': {'id': {'type': 'string'}, 'tool': {'type': 'string'}, 'label': {'type': 'string'}, 'reason': {'type': 'string'}, 'arguments': {'type': 'object', 'additionalProperties': True}, 'arguments_from': {'type': 'string'}}, 'additionalProperties': False}}, 'allow_freeform': {'type': 'boolean'}, 'next_steps_text': {'type': 'string'}}, 'additionalProperties': False}, 'map_types': {'type': ['null', 'array'], 'items': {'type': 'string'}}, 'geo_levels': {'type': ['null', 'array'], 'items': {'type': 'string'}}, 'index_modes': {'type': ['null', 'array'], 'items': {'type': 'string'}}, 'svg_default': {'type': 'boolean'}, 'geo_detection': {'type': 'boolean'}, 'png_supported': {'type': 'boolean'}, 'default_output': {'type': 'string'}, 'output_formats': {'type': ['null', 'array'], 'items': {'type': 'string'}}, 'stateless_only': {'type': 'boolean'}, 'available_tools': {'type': ['null', 'array'], 'items': {'type': 'string'}}, 'available_indices': {'type': ['null', 'array'], 'items': {'type': 'string'}}, 'auto_level_detection': {'type': 'boolean'}, 'classification_methods': {'type': ['null', 'array'], 'items': {'type': 'string'}}}, 'additionalProperties': False}
question_to_map
Render a geographic map from your data. Call upload_file first to ingest a CSV/XLSX file, then pass the returned file_key: {"question": "show total by pincode", "file_key": "mcp-uploads/2026/01/15/<uuid>.csv"}. The file_key is reusable â for additional questions on the same file, pass the same file_key again without re-uploading.
Alternatively, pass rows directly as 'data' (e.g. from the upload_file preview): {"question": "show total by pincode", "data": [<rows>]}
geo_column and value_column are preferred if easily identifiable from the column names, but not required â the server auto-detects them using a geo-match service that matches your data against known Indian geographies (states, districts, ACs, PCs, pincodes, RTOs, cities).
Hints: geo_column usually contains place names (state, district, city, pincode, constituency) or numeric codes (pincode=560049); value_column usually contains the metric to plot (sales, count, total, percentage) or a category (party affiliation, status, type).
If value_column is 'total' and doesn't exist in data, all numeric columns are summed automatically. String value columns render as categorical maps (each distinct value gets its own color, e.g. party affiliation). If the server cannot determine which columns to use, it returns a 'clarification_required' response with suggested columns and guidance â pick a geo_column and value_column from the suggestions and re-call.
Supported geo types: state, district, pincode, rto, city, ac, pc.
Points mode: if your file has latitude/longitude columns (e.g. from a prior geocoding step, or coordinates you already had) and your question asks to plot/pin/mark locations ("plot my stores", "show me the outlet locations", "mark these points"), the server detects the coordinate columns automatically and renders markers instead of shaded regions â no geo_column/value_column needed. The boundary lines drawn beneath the markers and how far the map zooms are chosen automatically from where the points actually fall (tightens to a state or district for a local cluster, falls back to all-India for a nationwide spread); you can also name a level explicitly ("plot by assembly constituency", "rto wise") to force it. A response may report points that fall outside every boundary (e.g. offshore or swapped lat/lon) â check for that in the warnings.
输入模式
{'type': 'object', 'required': ['question'], 'properties': {'data': {'type': ['null', 'array'], 'items': {'type': 'object', 'additionalProperties': True}}, 'sheet': {'type': 'string'}, 'style': {'type': 'object', 'additionalProperties': True}, 'table': {'type': 'string'}, 'file_key': {'type': 'string'}, 'map_type': {'type': 'string'}, 'question': {'type': 'string'}, 'geo_level': {'type': 'string'}, 'geo_column': {'type': 'string'}, 'state_hint': {'type': ['null', 'array'], 'items': {'type': 'string'}}, 'enrich_with': {'type': ['null', 'object'], 'required': ['table', 'columns'], 'properties': {'table': {'type': 'string'}, 'columns': {'type': ['null', 'array'], 'items': {'type': 'string'}}, 'join_on': {'type': 'string'}}, 'additionalProperties': False}, 'detected_geo': {'type': ['null', 'object'], 'required': ['column', 'geo_type', 'confidence', 'matched_count', 'total_count'], 'properties': {'column': {'type': 'string'}, 'parent': {'type': 'string'}, 'geo_type': {'type': 'string'}, 'confidence': {'type': 'number'}, 'total_count': {'type': 'integer'}, 'matched_count': {'type': 'integer'}}, 'additionalProperties': False}, 'value_column': {'type': 'string'}, 'output_format': {'type': 'string'}, 'render_params': {'type': 'object', 'additionalProperties': True}}, 'additionalProperties': False}
输出模式
{'type': 'object', 'required': ['mode'], 'properties': {'map': {'type': ['null', 'object'], 'required': ['url', 'format', 'render_id'], 'properties': {'url': {'type': 'string'}, 'title': {'type': 'string'}, 'format': {'type': 'string'}, 'svg_url': {'type': 'string'}, 'html_url': {'type': 'string'}, 'subtitle': {'type': 'string'}, 'geo_scope': {'type': 'string'}, 'mime_type': {'type': 'string'}, 'render_id': {'type': 'string'}, 'dimensions': {'type': ['null', 'object'], 'required': ['width', 'height'], 'properties': {'width': {'type': 'integer'}, 'height': {'type': 'integer'}}, 'additionalProperties': False}, 'expires_at': {'type': 'string'}, 'total_count': {'type': 'integer'}, 'total_value': {'type': 'string'}, 'value_label': {'type': 'string'}, 'active_count': {'type': 'integer'}, 'join_summary': {'type': ['null', 'object'], 'required': ['matched', 'unmatched', 'total'], 'properties': {'total': {'type': 'integer'}, 'matched': {'type': 'integer'}, 'unmatched': {'type': 'integer'}}, 'additionalProperties': False}, 'preview_image_base64': {'type': 'string'}}, 'additionalProperties': False}, 'mode': {'type': 'string'}, 'usage': {'type': ['null', 'object'], 'required': ['this_call', 'breakdown', 'month_used', 'month_limit', 'month_remaining'], 'properties': {'breakdown': {'type': 'object', 'additionalProperties': {'type': 'integer'}}, 'this_call': {'type': 'integer'}, 'month_used': {'type': 'integer'}, 'month_limit': {'type': 'integer'}, 'month_remaining': {'type': 'integer'}}, 'additionalProperties': False}, 'guidance': {'type': ['null', 'object'], 'required': ['next_actions', 'allow_freeform', 'next_steps_text'], 'properties': {'next_actions': {'type': ['null', 'array'], 'items': {'type': 'object', 'required': ['id', 'label', 'tool'], 'properties': {'id': {'type': 'string'}, 'tool': {'type': 'string'}, 'label': {'type': 'string'}, 'reason': {'type': 'string'}, 'arguments': {'type': 'object', 'additionalProperties': True}, 'arguments_from': {'type': 'string'}}, 'additionalProperties': False}}, 'allow_freeform': {'type': 'boolean'}, 'next_steps_text': {'type': 'string'}}, 'additionalProperties': False}, 'warnings': {'type': ['null', 'array'], 'items': {'type': 'string'}}, 'confidence': {'type': 'number'}, 'clarification': {'type': ['null', 'object'], 'required': ['status', 'message', 'columns', 'suggested_geo_columns', 'suggested_value_columns'], 'properties': {'status': {'type': 'string'}, 'columns': {'type': ['null', 'array'], 'items': {'type': 'object', 'required': ['name', 'type', 'class'], 'properties': {'name': {'type': 'string'}, 'type': {'type': 'string'}, 'class': {'type': 'string'}}, 'additionalProperties': False}}, 'message': {'type': 'string'}, 'guidance': {'type': ['null', 'object'], 'required': ['next_actions', 'allow_freeform', 'next_steps_text'], 'properties': {'next_actions': {'type': ['null', 'array'], 'items': {'type': 'object', 'required': ['id', 'label', 'tool'], 'properties': {'id': {'type': 'string'}, 'tool': {'type': 'string'}, 'label': {'type': 'string'}, 'reason': {'type': 'string'}, 'arguments': {'type': 'object', 'additionalProperties': True}, 'arguments_from': {'type': 'string'}}, 'additionalProperties': False}}, 'allow_freeform': {'type': 'boolean'}, 'next_steps_text': {'type': 'string'}}, 'additionalProperties': False}, 'suggested_geo_columns': {'type': ['null', 'array'], 'items': {'type': 'string'}}, 'suggested_value_columns': {'type': ['null', 'array'], 'items': {'type': 'string'}}}, 'additionalProperties': False}, 'computed_data': {'type': ['null', 'object'], 'required': ['columns', 'rows', 'total_rows', 'returned_rows'], 'properties': {'rows': {'type': ['null', 'array'], 'items': {'type': 'object', 'additionalProperties': True}}, 'columns': {'type': ['null', 'array'], 'items': {'type': 'string'}}, 'sort_order': {'type': 'string'}, 'total_rows': {'type': 'integer'}, 'sort_column': {'type': 'string'}, 'returned_rows': {'type': 'integer'}}, 'additionalProperties': False}, 'rejected_rows': {'type': ['null', 'array'], 'items': {'type': 'string'}}, 'points_plotted': {'type': 'integer'}, 'points_rejected': {'type': 'integer'}, 'resolved_params': {'type': ['null', 'object'], 'required': ['map_type', 'geo_type', 'geo_column', 'value_column'], 'properties': {'style': {'type': 'object', 'additionalProperties': True}, 'geo_type': {'type': 'string'}, 'map_type': {'type': 'string'}, 'geo_column': {'type': 'string'}, 'state_hint': {'type': ['null', 'array'], 'items': {'type': 'string'}}, 'value_column': {'type': 'string'}}, 'additionalProperties': False}, 'level_resolution': {'type': ['null', 'object'], 'required': ['datasets', 'resolved_level', 'method'], 'properties': {'method': {'type': 'string'}, 'reason': {'type': 'string'}, 'datasets': {'type': ['null', 'array'], 'items': {'type': 'object', 'required': ['source', 'level', 'geo_col', 'rows'], 'properties': {'rows': {'type': 'integer'}, 'level': {'type': 'string'}, 'source': {'type': 'string'}, 'geo_col': {'type': 'string'}}, 'additionalProperties': False}}, 'aggregations': {'type': ['null', 'array'], 'items': {'type': 'object', 'required': ['dataset', 'from_level', 'to_level', 'method', 'columns'], 'properties': {'method': {'type': 'string'}, 'columns': {'type': ['null', 'array'], 'items': {'type': 'string'}}, 'dataset': {'type': 'string'}, 'to_level': {'type': 'string'}, 'from_level': {'type': 'string'}}, 'additionalProperties': False}}, 'alternatives': {'type': ['null', 'array'], 'items': {'type': 'string'}}, 'resolved_level': {'type': 'string'}}, 'additionalProperties': False}, 'resolution_method': {'type': 'string'}, 'aggregation_applied': {'type': ['null', 'object'], 'required': ['original_rows', 'aggregated_rows', 'method', 'note'], 'properties': {'note': {'type': 'string'}, 'method': {'type': 'string'}, 'grouped_by': {'type': 'string'}, 'original_rows': {'type': 'integer'}, 'aggregated_rows': {'type': 'integer'}}, 'additionalProperties': False}}, 'additionalProperties': False}
reverse_geocode
Turn latitude/longitude coordinates in an uploaded CSV or XLSX into addresses. Pass a file_key from upload_file.
Column selection: pass latitude_column and longitude_column, or latlong_column for a single combined column, or coordinate_pairs for several sets. Otherwise the server detects them. When the answer is not unambiguous â several coordinate columns, a weak match, coordinates outside the supported region, or coarse whole-number values â the response is status=clarification_required. Present the choice to the user and re-call with their answer.
Results are written back into the uploaded file: the response carries file_key, a signed file_url, and a small preview. Each pair adds address, pincode, landmark and status columns.
输入模式
{'type': 'object', 'properties': {'sheet': {'type': 'string'}, 'job_id': {'type': 'string'}, 'file_key': {'type': 'string'}, 'force_new': {'type': 'boolean'}, 'latlong_column': {'type': 'string'}, 'latitude_column': {'type': 'string'}, 'coordinate_pairs': {'type': ['null', 'array'], 'items': {'type': 'object', 'properties': {'latlong_column': {'type': 'string'}, 'latitude_column': {'type': 'string'}, 'longitude_column': {'type': 'string'}}, 'additionalProperties': False}}, 'longitude_column': {'type': 'string'}, 'accept_low_precision': {'type': 'boolean'}}, 'additionalProperties': False}
upload_file
Upload a CSV or XLSX file for server-side ingestion under policy (auth, size cap, malware scan, injected-intent scan) and receive a file_key. This is the first step before calling question_to_map with your own data.
Step 1: Call upload_file with file_name, file_size, and question. The response contains a signed upload_url and a curl command â run the curl command to POST the file bytes. The curl response contains file_key, columns, a row preview, and optionally 'intent' (geo-match service detected geo_column, geo_level, value_column, map_type, confidence) and 'guidance' (a ready-to-fire question_to_map action with all detected parameters pre-filled). Forward the intent fields into question_to_map to skip auto-detection and render immediately.
For multi-sheet XLSX: the curl response is 'sheet_selection_required' with a file_key and sheets list. Pass file_key and sheet to question_to_map (or any other tool that accepts file_key) â do not call upload_file again.
After upload, pass the returned file_key to question_to_map to render a map from the full dataset. The file_key is reusable â for additional questions on the same file, pass the same file_key again without re-uploading. When the upload response includes 'guidance', use its next_actions directly for a zero-round-trip render.
输入模式
{'type': 'object', 'required': ['file_name', 'file_size'], 'properties': {'sheet': {'type': 'string'}, 'file_key': {'type': 'string'}, 'question': {'type': 'string'}, 'file_name': {'type': 'string'}, 'file_size': {'type': 'integer'}}, 'additionalProperties': False}
历史
近期工具变更
已添加
reverse_geocode
2026年9月27日 02:51
已更改
question_to_map
2026年9月27日 02:51
已添加
geocode
2026年9月27日 02:51
已移除
process_upload
2026年9月25日 03:00
已移除
create_upload
2026年9月25日 03:00
已添加
upload_file
2026年9月25日 03:00
已更改
question_to_map
2026年9月25日 03:00
已更改
get_capabilities
2026年9月25日 03:00
已添加
question_to_map
2026年9月17日 07:57
已添加
process_upload
2026年9月17日 07:57
已添加
get_capabilities
2026年9月17日 07:57
已添加
create_upload
2026年9月17日 07:57
发现
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