LatLong Maps

ai.latlong由 latlong.ai 发布官方发布者v1.0更新于 Oct 24, 2026

Turn CSV or Excel data into styled choropleth and categorical maps of India from plain language.

已验证Streamable HTTP可网页运行位置与生活数据与分析

概览

AI 生成的概览

远程 MCP 服务器,把上传的 CSV 或 Excel 数据转成印度地图,并提供地理编码、逆地理编码和自然语言地图问答。

可以做什么
  • 上传 CSV 或 XLSX 文件供服务端摄取,返回可复用的 file_key
  • 把上传文件中的地址地理编码为经纬度,可指定或自动识别地址列
  • 把上传文件中的经纬度逆地理编码为地址
  • 根据自然语言问题和 file_key 生成分级设色或分类地图
  • 返回服务器能力矩阵:地图类型、地理类型、输出格式和可用工具
适用场景
适合手上有含地址、邮编或坐标的表格数据、想不写 GIS 代码就得到印度地图的场景,也适合让助手通过生成地图来回答位置相关问题。
运行要求
远程 streamable HTTP 端点,无需本地安装包。需要 LatLong 访问令牌(JWT),通过 X-Authorization-Token 请求头发送。上传文件在服务端按大小上限和扫描策略处理。
安装前请注意
每次调用都必须提供 X-Authorization-Token 这一密钥。上传的 CSV/XLSX 数据会发送给第三方服务进行摄取、扫描和制图,请避免上传敏感或个人信息。上传和制图会在服务端写入数据。

安装

在 SourceWeft 中

  1. 打开 控制台中的 LatLong Maps,将其添加到工作区。
  2. 连接 X-Authorization-Token 凭据。它们会被私密存储,绝不会在公开页面上展示。
  3. 为需要使用其工具的对话启用该服务。

Web executable,通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体,或 SourceWeft 对话中。智能体会读取该服务器的安装指南,先向你展示发布者、端点和登录方式,经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/ai.latlong%2Fmap-mcp/install.md,并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前,先告诉我它的发布者、端点和登录方式,等我确认。不要把 API 密钥写进命令或我会提交的文件;问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "mcpServers": {
    "latlong-maps": {
      "type": "http",
      "url": "https://geocp.latlong.ai/mcp/",
      "headers": {
        "X-Authorization-Token": "<YOUR_API_KEY>"
      }
    }
  }
}

工具

5

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 — several address columns, a weak match, or only structured columns like District/State/PIN that must be combined — 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.

输入参数sheetjob_idfile_keyforce_newaddress_columnaddress_columnscompose_columns

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.

无需输入参数。

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/<user-id>/2026/01/15/<uuid>.csv"}. The file_key is reusable — 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 — 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 — 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 — 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

输入参数datasheetstyletablefile_keymap_typequestion必填geo_levelgeo_columnstate_hintenrich_withdetected_geo另有 3 个

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 — several coordinate columns, a weak match, coordinates outside the supported region, or coarse whole-number values — 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.

输入参数sheetjob_idfile_keyforce_newlatlong_columnlatitude_columncoordinate_pairslongitude_columnaccept_low_precision

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 — 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) — 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 — 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.

输入参数sheetfile_keyquestionfile_name必填file_size必填

版本历史

1
  1. v1.0最新Sep 16, 2026

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