fal MCP Server Connect your AI to 1,000+ models
A hosted endpoint that lets any AI assistant search, run, and chain more than 1,000 generative AI models directly from a conversation — image, video, audio, 3D, upscaling, and more. One command. No SDK. No docs to read.
Up and running in two steps
The fal MCP Server is hosted — there is nothing to install and nothing to run locally. Point your client at mcp.fal.ai, sign in with your fal account, and start generating.
1. Add the fal MCP server to your client
- Open the fal plugin page. Click Install plugin.
- Select Connect for the fal app if prompted. Complete browser sign-in and authorization.
2. Ask for anything
In a new chat, type @fal.
That's it. Your assistant now has access to every model on fal — it searches the catalog, checks pricing, picks the right model, and runs it, all in one turn.
Read the MCP docs
Setup guides for every client, the full tool reference, and troubleshooting tips.
Open the Docs →MCP & connectors
Choose which fal account your assistant bills: your personal account or a team.
Open Dashboard →Join the fal Discord
Share what you build, get help from the team, and see what others are running through MCP.
Join Discord →
Tools, zero configuration
The MCP server exposes tools that your assistant picks automatically. You never call them directly — you just describe what you want.
search_modelsSearch the catalog by keyword or category.
recommend_modelDescribe the task; get candidate models and published guidance.
browse_modelsBrowse trending and recently added models by category.
get_model_schemaRead a model's exact inputs and outputs before running it.
search_guidance, read_guidance, search_docsRead fal's prompting and model-routing guidance and docs.
get_pricingUnit prices for up to 50 models in one call.
estimate_costEstimate one request from its real inputs. Advisory, not a quote.
list_model_setsUse your Sandbox model sets for side-by-side comparisons.
check_account_status, list_accounts, select_accountSee which fal account is billed and switch to a team you belong to.
run_modelRun a model and wait a bounded time for the result.
submit_job, submit_jobsQueue long video and 3D jobs, or up to 8 requests at once.
check_job, wait_for_jobsFollow queued jobs without resubmitting them.
get_job_result, cancel_jobFetch finished output, or cancel a job you no longer need.
upload_fileUpload from a public URL, base64, or a signed upload up to 90 MB.
upload_attachment, verify_uploadImport a chat attachment where the app allows it, and confirm the upload.
inspect_imagesLook at up to 4 existing images before editing them.
prepare_media_workflowCheck up to 8 chained steps against live schemas before spending credits.
prepare_media_transformPlan an upscale, cutout, HDR delivery or video outpaint.
resolve_model_inputsTurn saved assets, characters and past jobs into valid model inputs.
prepare_deliverablesCheck up to 32 outputs against the delivery you asked for.
save_job_outputs, save_assetSave results to your fal library, keeping where they came from.
search_assets, list_collectionsFind saved media and organize it into collections.
create_entity, list_entitiesKeep reusable characters, props, environments, styles and scenes.
search_request_history, get_job_recipeFind past generations from the last 90 days and their exact settings.
show_media, open_libraryPreview results and browse your library in apps that display MCP Apps, such as ChatGPT.
What can you do with it?
Real prompts, straight from a conversation. Copy one into your assistant and watch it pick the models, run them, and hand back the results.
Generate images with the best model
Your assistant searches the catalog, finds the most suitable model, checks the parameters, and runs it — all in one turn. The assistant picks the model. You describe the result.
Chain LLM + image generation
An LLM writes a detailed prompt with lighting, camera angle, and mood — then the image model generates with it. Dramatically better than a simple prompt.
Turn a photo into a video
Upload → model selection → queue management → result. All handled automatically.
Run a full creative pipeline
Three models chained in one conversation: an LLM writes the copy, an image model generates the visual, a TTS model reads the tagline. You get all the URLs back.
Compare models side by side
Run both, see both results. Model benchmarking in one sentence.
Check pricing before you run
Know the cost before you spend.
Chain complex workflows
Chain generation → upscaling → analysis in a single conversation.
Stateless, isolated, and free to use
Your assistant sends a request to mcp.fal.ai signed in with your fal account, the server calls the fal Platform API on your behalf, and the results come straight back to your conversation.
Hosted and stateless
The server is fully stateless and hosted on Vercel. No sessions, no state — each request is completely isolated.
No API key to paste
Your client signs in to your fal account in the browser. There is no key to create, paste into a config file, or rotate.
Free to use
The MCP server is free. You only pay for the model runs you trigger, at standard fal pricing.
Common questions about the fal MCP Server
What is the fal MCP Server?
The fal MCP Server is a hosted endpoint at https://mcp.fal.ai/mcp-relay that lets any AI assistant search, run, and chain 1,000+ generative AI models on fal directly from a conversation. It is built on the Model Context Protocol (MCP), an open standard that lets AI assistants use external tools. Your assistant doesn't just generate code that calls fal — it actually calls fal.
Which AI assistants are supported?
Any MCP-compatible client with OAuth sign-in works. ChatGPT, Claude, Claude Code, Codex, Cursor, and Grok Bot have setup steps on this page; Cursor and Grok Bot install fal as a plugin from the Cursor Marketplace. Other clients connect to the same server URL.
Do I need an API key?
No. Your client signs in to your fal account in the browser, so there is no key to create or paste. Only clients that can't sign in with MCP OAuth need a key: send it as Authorization: Key <key_id>:<key_secret> to the same URL.
I set it up with an API key before. What changed?
The old setup used an API key in an Authorization header and now fails with 401. Remove that fal entry from your client and add https://mcp.fal.ai/mcp-relay again without a header, then sign in in the browser.
How much does it cost?
The MCP server itself is free. You only pay for the model runs your assistant triggers, at standard fal pricing — the same rates as calling the API directly. Your assistant can even check the price of a model with the get_pricing tool before running it.
Which fal account gets billed?
Your Active MCP account: your personal account by default. To use a team's credits, select the team in MCP & connectors (fal.ai/dashboard/ai-tools). New requests switch without reconnecting.
What tools does the server expose?
Tools for finding models (search_models, recommend_model), checking prices (get_pricing, estimate_cost), running and queueing generations (run_model, submit_job, check_job), uploading inputs (upload_file), and saving results to your fal library. Your assistant picks them automatically — you just describe what you want.
What kinds of models can I run?
The entire fal catalog: image generation, video generation, audio and text-to-speech, 3D, upscaling, LLMs, vision models, and more — over 1,000 models in total.
Can my assistant chain multiple models together?
Yes — that's one of the biggest advantages. A single request like "generate a futuristic Tokyo street, upscale it to 4K, and describe what's in it" chains generation, upscaling, and analysis in one conversation. The assistant handles uploads, queueing, and passing results between models.
Can I use my own images?
Yes. Give your assistant a public URL, or attach the file in an app that shares attachments with tools. The hosted server can't read files on your computer.
The fal tools don't show up. What should I check?
Finish the browser sign-in, then start a new chat or session. In Claude, enable fal from the + menu under Connectors; in ChatGPT, type @fal. Check that the server URL is https://mcp.fal.ai/mcp-relay and that no Authorization header is set.
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