FastMCP - Model Context Protocol Server

Name: FastMCP - Model Context Protocol Server
Author: ryuichi1208

@ryuichi1208

代码仓库

关于 FastMCP - Model Context Protocol Server

暂无概览

#datadog #mcp #model-context-protocol

基本信息

分类

开发工具

运行时

python

传输方式

stdio

发布者

ryuichi1208

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"
      ]
    }
  }
}

工具

未检测到工具

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

FAQ from FastMCP - Model Context Protocol Server

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.

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.

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.

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.

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.