Fal MCP Server

What raveenb/fal-mcp-server MCP server does

The raveenb/fal-mcp-server MCP server exposes Fal.ai media-generation capabilities to Claude Desktop and other MCP clients. It supports image creation from prompts, structured image generation, image-to-image transformation, video generation, image animation, video restyling, and instrumental or vocal music generation.

Image editing tools cover background removal, upscaling, natural-language edits, masked inpainting, social-media-oriented resizing, and image composition for overlays such as logos or watermarks. The server also includes tools for uploading local files and checking available models, estimated costs, and account usage.

This fits the Multimedia Process category for workflows where an agent needs to create or modify visual or audio assets through Fal.ai rather than calling separate media APIs manually.

How it works

The server uses Fal.ai's API and accepts a Fal.ai API key through the FAL_KEY environment variable. Its implementation uses asynchronous requests, while longer video and music jobs use Fal.ai's queue workflow and can provide progress updates.

Model selection is dynamic. The list_models tool can search the platform's catalog, which contains more than 600 models according to the project documentation. Generation requests can use complete Fal.ai model identifiers or supported friendly aliases. A cache reduces repeated model-discovery requests.

Communication can use traditional MCP STDIO, HTTP with Server-Sent Events, or both modes at once. For HTTP/SSE deployments, an MCP client connects to the server endpoint rather than launching the process directly.

Setup and configuration

The raveenb/fal-mcp-server MCP server requires Python 3.10 or newer and a Fal.ai API key. It can be run with uvx, installed from PyPI with pip, installed from source, or launched from the published Docker image. Claude Desktop configuration can start the fal-mcp command directly for a local installation.

Docker uses HTTP by default and exposes port 8080. The FAL_MCP_TRANSPORT variable selects http, stdio, or dual; FAL_MCP_HOST controls the bind address; and FAL_MCP_PORT changes the HTTP port. Docker users who need upload_file must mount host directories and provide paths as seen inside the container.

Claude Code users can install the documented fal-ai plugin through the repository's marketplace or plugin source. Any deployment should keep the Fal.ai key outside prompts and source-controlled configuration.

Tools and capabilities

The documented tool groups include:

• Image generation, structured generation, and image-to-image transformation
• Background removal, upscaling, editing, inpainting, resizing, and composition
• Text-to-video, image-to-video, and video-to-video generation
• Music generation
• Model listing and recommendation
• Pricing and usage lookup
• Local file upload for generation workflows

Limitations and notes

The server depends on Fal.ai availability, model support, and account permissions, so outputs and costs depend on the selected Fal.ai model. The documentation describes a free Fal.ai tier but does not define universal quotas or model-specific pricing.

File access differs by transport. A local STDIO process can use the available filesystem, while a Docker deployment requires explicit volume mounts and container-visible paths. HTTP/SSE deployments also require an MCP client that can connect through the configured endpoint.