FastMCP

FastMCP - Model Context Protocol Server

A lightweight Model Context Protocol (MCP) server implemented with FastMCP, a fast and Pythonic framework for building MCP servers and clients.

Features

Create, retrieve, update, and delete model contexts
Query execution against specific contexts
Filtering by model name and tags
In-memory storage (for development)
FastMCP integration for easy MCP server development
Datadog integration for metrics and monitoring

Requirements

Python 3.7+
FastMCP
uv (recommended for environment management)
Datadog account (optional, for metrics)

Installation

Using uv (Recommended)

The simplest way to install is using the provided scripts:

Unix/Linux/macOS

# Clone the repository
git clone https://github.com/yourusername/datadog-mcp-server.git
cd datadog-mcp-server

# Make the install script executable
chmod +x install.sh

# Run the installer
./install.sh

Windows

# Clone the repository
git clone https://github.com/yourusername/datadog-mcp-server.git
cd datadog-mcp-server

# Run the installer
.\install.ps1

Manual Installation

# Clone the repository
git clone https://github.com/yourusername/datadog-mcp-server.git
cd datadog-mcp-server

# Create and activate a virtual environment with uv
uv venv
# On Unix/Linux/macOS:
source .venv/bin/activate
# On Windows:
.\.venv\Scripts\activate

# Install dependencies
uv pip install -r requirements.txt

Datadog Configuration

The server integrates with Datadog for metrics and monitoring. You can configure Datadog API credentials in several ways:

1. Environment Variables

Set these environment variables before starting the server:

# Unix/Linux/macOS
export DATADOG_API_KEY=your_api_key
export DATADOG_APP_KEY=your_app_key  # Optional
export DATADOG_SITE=datadoghq.com    # Optional, default: datadoghq.com

# Windows PowerShell
$env:DATADOG_API_KEY = 'your_api_key'
$env:DATADOG_APP_KEY = 'your_app_key'  # Optional
$env:DATADOG_SITE = 'datadoghq.com'    # Optional

2. .env File

Create a .env file in the project directory:

DATADOG_API_KEY=your_api_key
DATADOG_APP_KEY=your_app_key
DATADOG_SITE=datadoghq.com

3. FastMCP CLI Installation

When installing as a Claude Desktop tool, you can pass environment variables:

fastmcp install mcp_server.py --name "Model Context Server" -v DATADOG_API_KEY=your_api_key

4. Runtime Configuration

Use the configure_datadog tool at runtime:

result = await client.call_tool("configure_datadog", {
    "api_key": "your_api_key",
    "app_key": "your_app_key",  # Optional
    "site": "datadoghq.com"     # Optional
})

Usage

Starting the Server

# Start directly from activated environment
python mcp_server.py

# Or use uv run (no activation needed)
uv run python mcp_server.py

# Use FastMCP CLI for development (if in activated environment)
fastmcp dev mcp_server.py

# Use FastMCP CLI with uv (no activation needed)
uv run -m fastmcp dev mcp_server.py

Installing as a Claude Desktop Tool

# From activated environment
fastmcp install mcp_server.py --name "Model Context Server"

# Using uv directly
uv run python -m fastmcp install mcp_server.py --name "Model Context Server"

# With Datadog API key
fastmcp install mcp_server.py --name "Model Context Server" -v DATADOG_API_KEY=your_api_key

Using the Tools

The server provides the following tools:

create_context - Create a new context
get_context - Retrieve a specific context
update_context - Update an existing context
delete_context - Delete a context
list_contexts - List all contexts (with optional filtering)
query_model - Execute a query against a specific context
health_check - Server health check
configure_datadog - Configure Datadog integration at runtime

Example Requests

Creating a Context

result = await client.call_tool("create_context", {
    "context_id": "model-123",
    "model_name": "gpt-3.5",
    "data": {
        "parameters": {
            "temperature": 0.7
        }
    },
    "tags": ["production", "nlp"]
})

Executing a Query

result = await client.call_tool("query_model", {
    "context_id": "model-123",
    "query_data": {
        "prompt": "Hello, world!"
    }
})

Configuring Datadog

result = await client.call_tool("configure_datadog", {
    "api_key": "your_datadog_api_key",
    "app_key": "your_datadog_app_key",  # Optional
    "site": "datadoghq.com"             # Optional
})

Datadog Metrics

The server reports the following metrics to Datadog:

mcp.contexts.created - Context creation events
mcp.contexts.updated - Context update events
mcp.contexts.deleted - Context deletion events
mcp.contexts.accessed - Context access events
mcp.contexts.total - Total number of contexts
mcp.contexts.listed - List contexts operation events
mcp.queries.executed - Query execution events
mcp.server.startup - Server startup events
mcp.server.shutdown - Server shutdown events

Development

See the included mcp_example.py for a client implementation example:

# Run the example client (with activated environment)
python mcp_example.py

# Run with uv (no activation needed)
uv run python mcp_example.py

License

MIT

Resources

This server cannot be installed

security - not tested

license - not found

quality - not tested

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

A lightweight Model Context Protocol server that enables creating, managing, and querying model contexts with integrated Datadog metrics and monitoring.

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