arcgis-mcp

arcgis-mcp

Enables querying and metadata retrieval from ArcGIS REST services, allowing users to interact with geospatial data layers through natural language.

Category
访问服务器

README

arcgis-mcp

Generic MCP connector for querying ArcGIS REST services - built so it works against RRC's well/pipeline data today, and your own ArcGIS Online account later (once you have a login), without any code changes.

Why generic, not hardcoded to one URL

RRC's own statewide GIS service (gis.rrc.texas.gov) exists but its exact service path wasn't confirmed directly (an automated-access block on that one page stopped me checking it myself). What is confirmed and verified live is a regional mirror of the same TXRRC well/pipeline data, hosted by Houston-Galveston Area Council - see KNOWN_SERVICES in src/arcgis_client.py. That mirror's extent suggests Houston-area coverage rather than confirmed statewide, so treat it as a starting point, and swap in RRC's real statewide URL the moment you confirm it (same tool works against any ArcGIS REST MapServer/FeatureServer URL).

Tools exposed

  • list_known_services() - pre-verified service URLs and layer notes.
  • get_layer_metadata(service_url, layer_id?, token?) - field names, geometry type, extent for a service or one layer in it. Call this first against any new/unknown service so you know what to filter on.
  • query_arcgis_layer(service_url, layer_id, where, out_fields, bbox?, token?)
    • runs a standard Esri REST query, returns GeoJSON. token is optional and only needed for private/ArcGIS Online services later.

Verified so far

get_layer_metadata was checked directly against the live HGAC TXRRC/Wells service and returned real field names (API, WELLID, LONG83, LAT83, etc. on the Surface Wells layer). The actual query_arcgis_layer data call follows the standard, widely-documented Esri REST query spec, but wasn't hit live before handoff - it should work as-is; if a response looks off, send me the error/output and I'll adjust.

Run locally

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python main.py

Test:

curl -X POST http://localhost:3000/mcp \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"query_arcgis_layer","arguments":{"service_url":"https://www.gis.hctx.net/arcgishcpid/rest/services/TXRRC/Wells/MapServer","layer_id":0,"where":"1=1","out_fields":"API,WELLID","bbox":"-96.9,29.0,-96.5,29.4"}}}'

Deploy

Same pattern as before:

git init && git add . && git commit -m "Initial ArcGIS MCP connector"
git remote add origin https://github.com/<you>/arcgis-mcp.git
git push -u origin main

Render dashboard -> New -> Blueprint -> point at the repo (picks up render.yaml) -> deploy. Endpoint will be https://arcgis-mcp.onrender.com/mcp.

Adding your own ArcGIS Online account later

Once you have a login: generate a token (ArcGIS Online -> your account -> generate API key/token, or OAuth if you want it to expire/refresh), then pass it as the token argument on query_arcgis_layer / get_layer_metadata calls against your own hosted feature layers - no redeploy needed, it's just a per-call argument.

Combining with Enverus isopach data

This connector only fetches ArcGIS layers (wells, pipelines, or whatever else you point it at). Combining that with Enverus's isopach output into one map happens at the chat level - ask Claude to pull both and render them together once you're connected to this and have wells/pipelines to overlay.

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选