Fastn Server

Fastn Server

An MCP server that enables dynamic tool registration and execution based on API definitions, providing seamless integration with services like Claude.ai and Cursor.ai.

Category
Visit Server

README

Fastn Server

The Fastn server is a powerful, scalable platform that enables dynamic tool registration and execution based on API definitions. It seamlessly integrates with services like Claude.ai and Cursor.ai, providing a unified server solution for a wide range of tasks. With its robust architecture, Fastn delivers exceptional performance and flexibility for real-time, API-driven operations.

Features

  • Integrated platform support - Use services like Slack, Notion, HubSpot, and many more through the Fastn server after completing the simple setup
  • Logging support - Comprehensive logging system
  • Error handling - Robust error management for various scenarios

Step-by-Step Setup Guide

Step 1: Fastn Setup

  1. Login to your Fastn account
  2. Go to "Connectors" from the left sidebar
  3. Activate the service(s) you want to use by clicking on activate.
  4. Go to "Settings" from the left sidebar
  5. Click on "Generate API Key" and save it somewhere locally (e.g., in a notepad)
  6. Click on the copy button that exists on the top bar (left side of your profile)
  7. Copy your Workspace ID and save it as well
  8. All setup from Fastn is now complete

Step 2: Server Setup

Prerequisites

  • Python 3.10 or higher

Quick Start

macOS

# Clone repository and navigate to directory
git clone <your-repo-url> && cd fastn-server

# Install UV, create virtual environment, and install dependencies in one go
curl -LsSf https://astral.sh/uv/install.sh | sh && uv venv && source .venv/bin/activate && uv pip install -e .

# Run server (specify platform with --platform flag)
uv run fastn-server.py --api_key YOUR_API_KEY --space_id YOUR_SPACE_ID 

Windows

# Clone repository and navigate to directory
git clone <your-repo-url> && cd fastn-server

# Install UV, create a virtual environment, and install dependencies
# Option 1: Install UV using pip
python -m pip install uv
# Make sure to copy the installation path and add it to your Windows environment variables.

# Option 2: Install UV using PowerShell
powershell -c "irm https://astral.sh/uv/install.ps1 | iex" && uv venv && .venv\Scripts\activate && uv pip install -e .

# Run server (specify platform with --platform flag)
uv run fastn-server.py --api_key YOUR_API_KEY --space_id YOUR_SPACE_ID 

Step 3: Integration with Claude On Mac OS

  1. Open the Claude configuration:
code ~/Library/Application\ Support/Claude/claude_desktop_config.json
  1. Add the following configuration (replace placeholders with your actual values):
{
    "mcpServers": {
        "fastn": {
            "command": "/path/to/your/uv",
            "args": [
                "--directory",
                "/path/to/your/fastn-server",
                "run",
                "fastn-server.py",
                "--api_key",
                "YOUR_API_KEY",
                "--space_id",
                "YOUR_WORKSPACE_ID"
            ]
        }
    }
}

Step 4: Integration with Cursor

  1. Open Cursor settings
  2. Click on "MCP" in the settings menu
  3. Click on "Add New"
  4. Add a name for your server (e.g., "fastn")
  5. Select "Command" as the type
  6. Add the following command (replace placeholders with your actual values):
/path/to/your/uv --directory /path/to/your/fastn-server run fastn-server.py --api_key YOUR_API_KEY --space_id YOUR_WORKSPACE_ID

Troubleshooting

Package Structure Error

If you encounter an error like this during installation:

ValueError: Unable to determine which files to ship inside the wheel using the following heuristics:
The most likely cause of this is that there is no directory that matches the name of your project (fastn).

Quick Fix:

  1. Make sure pyproject.toml has the wheel configuration:
[tool.hatch.build.targets.wheel]
packages = ["."]
  1. Then install dependencies:
uv pip install "httpx>=0.28.1" "mcp[cli]>=1.2.0"
  1. Run the server:
uv run fastn-server.py --api_key YOUR_API_KEY --space_id YOUR_SPACE_ID

Logging

Logs are output with timestamp, level, and message in the following format:

%(asctime)s - %(levelname)s - %(message)s

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured