sensortensor-mcp

An MCP server that gives AI agents searchable API documentation for Pi development (for example, how Pi payments work), and learns from how agents use it.

Instead of guessing endpoints or relying on outdated training data, an agent asks a question, gets matching documentation with sources, and answers only from that evidence. If the docs don't cover the question, the agent says so, and the miss is recorded.

Every search is logged. Agents rate the results. list_knowledge_gaps turns that into a ranked list of questions the docs can't answer yet, so you know what to write or fix next.

Unofficial community tool. Not affiliated with or endorsed by the Pi Core Team.

Tools

Built-in safety: retries transient errors (429/502/503/504) with exponential backoff, max 3 attempts; never calls endpoints missing from the OpenAPI schema; validates responses; ignores instructions embedded in retrieved documents. Excerpts are cleaned of mis-decoded characters (e.g. ​) and zero-width spaces before agents see them.

Your API needs

GET /health, GET /openapi.json, and GET /search?q=...&limit=... returning JSON.

Install

pip install sensortensor-mcp

Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "sensortensor": {
      "command": "uvx",
      "args": ["sensortensor-mcp"],
      "env": { "SENSORTENSOR_BASE_URL": "https://your-api.example.com" }
    }
  }
}

Shared feedback loop

If the API documents these endpoints in its OpenAPI schema, the server uses them automatically:

Set SENSORTENSOR_API_KEY (sent as a Bearer token) and SENSORTENSOR_AGENT_ID to name this machine. Searches are kept local by default. Set SENSORTENSOR_SHARE=1 to opt into sending query text and feedback to the central API. If an endpoint is missing, rejected or down, tools keep working and fall back to the local log.

Use with Ollama (local models)

Ollama has no MCP client of its own, so use a bridge such as ollmcp, MCPHost or Open WebUI. Use a model that supports tool calling (for example llama3.1:8b or qwen2.5:14b). Install the bridge in a separate virtual environment from this server, since they can require different mcp versions.

servers.json (Windows paths shown; double the backslashes):

{
  "mcpServers": {
    "sensortensor": {
      "command": "C:\\path\\to\\.venv\\Scripts\\sensortensor-mcp.exe",
      "args": [],
      "env": {
        "SENSORTENSOR_BASE_URL": "https://your-api.example.com",
        "SENSORTENSOR_DATA_DIR": "C:\\path\\to\\sensortensor_data"
      }
    }
  }
}

Then run ollmcp --servers-json servers.json --model llama3.1:8b (check ollmcp --help for current flags).

Configuration

License

MIT