Which integrations are available for this server?

Provides metrics and monitoring integration, sending data about context operations (creation, updates, deletions, access), query executions, and server events to Datadog for observability and performance tracking. Allows configuration of the server through .env files, enabling storage of sensitive information like API keys outside of the codebase.

How do I use FastMCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@FastMCP create a context for our production gpt-4 model with temperature 0.8" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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

Related MCP server: PostgreSQL MCP Server

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

FastMCP