FastMCP - MCP Server

@ryuichi1208

Repository

Basic information

About FastMCP - MCP Server

No overview available yet

#datadog #mcp #model-context-protocol

FastMCP - MCP Server config

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

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

FastMCP - MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

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

Frequently asked questions

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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