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FastMCP - MCP Server

@ryuichi1208

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stdio

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ryuichi1208

关于 FastMCP - MCP Server

暂无概览

#datadog#mcp#model-context-protocol

FastMCP - MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

FastMCP - MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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

常见问题

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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