一个MCP服务器，为LLM代理提供了真实的地理空间计算工具，因此 答案中的空间主张是从指定来源计算的，而不是 从模型参数中回忆。

为什么

问一个语言模型两个地方相距多远，或者多重停车有多长 trip 需要，并且会得到一个有把握的数字。这个数字经常是错误的， 答案中也没有说明它来自哪里。

该服务器通过工具调用来回答这些问题。以下有两条规则：

1. 每个结果都带有其来源。 每个工具返回result， [source 和 params），这样调用者可以看到是什么产生了该值和 用什么设置？
2. 无静默备份。 如果某个地点无法解决或路由器无法解决 不可用时，工具会升高。它不会返回估计值，因为 这个服务器解决的问题是估计值读起来像测量值。

一个例子

佐治亚理工至亚特兰大皮埃蒙特公园：

驾驶路线是直线的1.75倍。

spatial-reasoning-mcp

An MCP server that gives an LLM agent real geospatial computation as tools, so that spatial claims in an answer are computed from a named source rather than recalled from the model’s parameters.

Why

Ask a language model how far apart two places are, or how long a multi-stop trip takes, and it will produce a confident number. The number is often wrong, and nothing in the answer says where it came from.

This server answers those questions with a tool call instead. Two rules follow:

1. Every result carries its provenance. Each tool returns result, source and params, so the caller can see what produced the value and with which settings.
2. No silent fallback. If a place cannot be resolved or a router is unavailable, the tool raises. It does not return an estimate, because an estimate that reads like a measurement is the problem this server addresses.

An example

Georgia Tech to Piedmont Park, Atlanta:

The driving route is 1.75 times the straight line. A model answering from memory gives one number somewhere in that range, with no way to tell which quantity it meant. Both numbers above say what they are and what computed them.

Tools

order_stops searches orderings exhaustively and accepts at most eight stops, so the order it returns is optimal for its travel-time matrix rather than a heuristic the caller has to trust.

Install and run

python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
python -m spatial_reasoning_mcp

The server communicates over stdio, so on its own it will sit there waiting for a client. Register it with one (Claude Desktop, Claude Code, Cursor):

{
  "mcpServers": {
    "spatial-reasoning": {
      "command": "/absolute/path/to/.venv/bin/python",
      "args": ["-m", "spatial_reasoning_mcp"],
      "cwd": "/absolute/path/to/spatial-reasoning-mcp"
    }
  }
}

To see the tools work without a client, run python scripts/smoke.py, which calls each one against live data and prints the results.

Routing backends

Set SRM_ROUTING_BACKEND to choose. All six tools work on either.

valhalla (default) calls a hosted OpenStreetMap router, by default the