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FastMCP - MCP Server

@ryuichi1208

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カテゴリ

開発者ツール

トランスポート

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ryuichi1208

FastMCP - MCP Server について

概要はまだありません

#datadog#mcp#model-context-protocol

FastMCP - MCP Server 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": {
    "datadog-mcp-server-ryuichi1208": {
      "command": "uv",
      "args": [
        "venv"
      ]
    }
  }
}

FastMCP - MCP Server 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 FastMCP - Model Context Protocol Server?

A lightweight MCP server built with the FastMCP framework that enables creating, retrieving, updating, and deleting model contexts, with optional Datadog integration for metrics and monitoring. It is designed for developers needing a simple in-memory context store for AI model interactions.

How to use FastMCP - Model Context Protocol Server?

Install via provided Unix/Windows scripts or manually using uv. Start the server with python mcp_server.py or uv run python mcp_server.py. Use tools such as create_context, get_context, update_context, delete_context, list_contexts, query_model, health_check, and configure_datadog. It can also be installed as a Claude Desktop tool via fastmcp install.

Key features of FastMCP - Model Context Protocol Server

  • Create, retrieve, update, and delete model contexts
  • Execute queries against specific contexts
  • Filter contexts by model name and tags
  • In-memory storage for development and testing
  • FastMCP integration for easy MCP server development
  • Datadog integration for metrics and monitoring

Use cases of FastMCP - Model Context Protocol Server

  • Manage model contexts for AI application development
  • Run queries on model contexts with filtering
  • Monitor server health and usage with Datadog
  • Prototype MCP servers locally with minimal setup
  • Integrate context management into Claude Desktop workflows

よくある質問

1

What are the runtime requirements?

Python 3.7+ and FastMCP are required. `uv` is recommended for environment management. A Datadog account is optional for metrics.

2

How do I configure Datadog integration?

Set environment variables `DATADOG_API_KEY`, `DATADOG_APP_KEY` (optional), and `DATADOG_SITE` (optional). Alternatively, use a `.env` file or the `configure_datadog` tool at runtime.

3

How do I install this server as a Claude Desktop tool?

Run `fastmcp install mcp_server.py --name "Model Context Server"` optionally passing Datadog credentials with `-v DATADOG_API_KEY=your_api_key`.

4

What tools does the server provide?

It provides eight tools: `create_context`, `get_context`, `update_context`, `delete_context`, `list_contexts`, `query_model`, `health_check`, and `configure_datadog`.

5

How do I start the server?

From the activated environment, run `python mcp_server.py`. Or use `uv run python mcp_server.py` without activation. For development, use `fastmcp dev mcp_server.py`.

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