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MCP Server for GraphQL Policies API

@Ad-Veritas

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基本情報

カテゴリ

開発者ツール

トランスポート

stdio

作者

Ad-Veritas

MCP Server for GraphQL Policies API について

Model Context Protocol (MCP) server to access an instance of TrueRAG

MCP Server for GraphQL Policies API の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "shipping-policies": {
      "command": "uv",
      "args": [
        "--directory",
        "{path_to_mcp_server}/mcp-server-trueRAG",
        "run",
        "fastmcp",
        "run",
        "server.py"
      ],
      "env": {
        "GRAPHQL_API_KEY": "",
        "GRAPHQL_ENDPOINT": ""
      }
    }
  }
}

MCP Server for GraphQL Policies API のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Model Context Protocol (MCP) Server for GraphQL Policies API?

It is an MCP server that provides access to policies via a GraphQL API, built using the Python MCP SDK and the GQL library. It is intended for users of TrueRAG systems who need to query policy data through a standardized model context protocol interface.

How to use Model Context Protocol (MCP) Server for GraphQL Policies API?

Clone the repository, ensure uv is installed, create a .env file with GRAPHQL_API_KEY and GRAPHQL_ENDPOINT from your TrueRAG environment, then configure your MCP client (e.g., Claude Desktop) using the uv --directory ... run fastmcp run server.py command as described in the README.

Key features of Model Context Protocol (MCP) Server for GraphQL Policies API

  • Built with Python MCP SDK and GQL library
  • Uses uv for dependency management and execution
  • Connects to GraphQL APIs for TrueRag systems
  • Configurable via environment variables for API key and endpoint
  • Provides access to policies through a standardized interface

Use cases of Model Context Protocol (MCP) Server for GraphQL Policies API

  • Querying policies from a TrueRAG system via Claude Desktop
  • Integrating policy data into AI workflows using MCP
  • Automating policy retrieval with AI assistants

よくある質問

1

What dependencies are required?

Python, uv, and the GQL library (managed automatically by uv).

2

How do I configure the server?

Set the `GRAPHQL_API_KEY` and `GRAPHQL_ENDPOINT` environment variables in a `.env` file placed in the repository root.

3

What MCP clients are supported?

The README provides a configuration example for Claude Desktop, but any MCP‑compatible client can be used.

4

Where does the data come from?

Data is accessed via a GraphQL API from a TrueRAG environment; the API key and endpoint must be supplied by the user.

5

What runtime environment is needed?

The server requires `uv` to run the `fastmcp` command; no other runtime or container is explicitly required.

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