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FastMCP - Model Context Protocol Server

@ryuichi1208

About FastMCP - Model Context Protocol Server

No overview available yet

Basic information

Category

Developer Tools

Runtime

python

Transports

stdio

Publisher

ryuichi1208

Config

Add this server to your MCP-compatible client using the configuration below.

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

Tools

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Overview

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.

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