falai-mcp-server

A FastMCP server that exposes core fal.ai model API operations (model catalogue, search, schema retrieval, inference, queue management, CDN uploads). The server can run locally over STDIO or remotely via the Streamable HTTP transport, and now ships with Docker support for easier deployment.

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Quick Start

PyPI Installation (Recommended)

pip install falai-mcp-tools

After installation, you can run the server with:

falai-mcp

Manual Installation

1. Clone the repository:

   git clone https://github.com/berkbirkan/falai-mcp.git
   cd falai-mcp
   

2. Create and activate a virtual environment:

   python3 -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   

3. Install the project in editable mode:

   pip install -e .
   

Requirements

• Python 3.10 or newer
• A fal.ai API key: either FAL_KEY or the FAL_KEY_ID/FAL_KEY_SECRET pair
• Docker (optional, only if you prefer containerized execution)

Configuration

Environment variables (prefixed with FALAI_) control runtime behaviour:

If you prefer a .env file, place it next to the project root (or mount it into the container) and load it before running the server.

Clients can override credentials and model filters per MCP session through the configure tool. Environment variables supply defaults when the client does not set overrides.

Usage

Local STDIO usage

1. Ensure your virtual environment is active and credentials are exported:

   export FAL_KEY=sk_live_...
   

2. Run the server with the default STDIO transport:

   falai-mcp
   

3. Leave the process running; configure your MCP client (Claude, Cursor, etc.) to launch this command via STDIO (see the client integration section).

Remote HTTP usage

1. Export credentials and enable the HTTP transport:

   export FAL_KEY=sk_live_...
   export FALAI_ENABLE_HTTP=true
   export FALAI_HTTP_PORT=8080  # optional override
   

2. Start the server so it listens on the configured host/port:

   falai-mcp
   

3. Confirm the HTTP transport is reachable (for example with curl -I http://localhost:8080/mcp/). Clients should connect to http://<host>:<port>/mcp/.

Docker Usage

1. Build the container image:

   docker build -t falai-mcp .
   

2. Run the container with HTTP enabled and publish the port:

   docker run \
     --rm \
     -e FAL_KEY=sk_live_... \
     -e FALAI_ENABLE_HTTP=true \
     -e FALAI_HTTP_PORT=8080 \
     -p 8080:8080 \
     falai-mcp
   

3. The MCP endpoint is now available at http://localhost:8080/mcp/.

Client integrations

Below are example configurations for popular MCP clients. Adjust paths, environment variables, and identifiers to match your setup.

Claude Desktop

Claude Desktop keeps its configuration in ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or the equivalent path on your platform.

• STDIO (local process)

  {
    "mcpServers": {
      "falai-local": {
        "command": "falai-mcp",
        "args": [],
        "env": {
          "FAL_KEY": "sk_live_..."
        }
      }
    }
  }
  

  Restart Claude Desktop after saving changes. Claude will spawn falai-mcp and communicate over STDIO.

• Remote HTTP server

  {
    "mcpServers": {
      "falai-remote": {
        "transport": {
          "type": "http",
          "url": "http://localhost:8080/mcp/"
        }
      }
    }
  }
  

Cursor

Cursor reads MCP configuration from ~/.cursor/mcp.json.

• STDIO (local process)

  {
    "clients": {
      "falai-local": {
        "command": "falai-mcp",
        "args": [],
        "env": {
          "FAL_KEY": "sk_live_..."
        }
      }
    }
  }
  

• Remote HTTP server

  {
    "clients": {
      "falai-remote": {
        "transport": {
          "type": "http",
          "url": "http://localhost:8080/mcp/"
        }
      }
    }
  }
  

After editing mcp.json, restart Cursor (or reload MCP connections) to pick up the new configuration.

Available tools

All tools enforce any configured allow-list and respect per-session overrides from the configure tool.

Development

Building for PyPI

1. Install build tools:

   pip install build twine
   

2. Build the package:

   python -m build
   

3. Upload to PyPI (test first with TestPyPI):

   # Test upload
   python -m twine upload --repository testpypi dist/*
   
   # Production upload
   python -m twine upload dist/*
   

Notes

• Schema retrieval and queue inspection require valid fal.ai credentials; errors appear as MCP tool errors if credentials are missing or invalid.
• Model discovery falls back to the bundled fal-client endpoint catalogue when fal.ai's public APIs are unavailable.
• When running remotely, ensure network access between the client and the MCP server (open firewall ports, configure TLS or reverse proxies if needed).

License

This project is licensed under the MIT License - see the LICENSE file for details.