fal.ai MCP Server

generate

Queue AI model generation requests to fal.ai and immediately get a request_id for tracking results.

Instructions

Submit a generation request to a fal.ai model. This queues the request and returns immediately with a request_id for tracking.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "app_id": "<app_id>",
      "input_data": "<input_data>"
    },
    "name": "generate"
  }
}

Input Schema

Name	Required	Description	Default
`app_id`	Yes	The model application ID (e.g., 'fal-ai/flux/dev')
`input_data`	Yes	Dictionary of input parameters for the model (model-specific)
`webhook_url`	No	Optional webhook URL for result notification
`output_format`	No	Output format (default: 'json')	json

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.1.4Jun 15, 2026

TDQS

C2.9/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must disclose behavioral traits. It only mentions queueing and returning a request_id. It omits important details like authentication needs, rate limits, potential delays, cost implications, or how to handle failures. The sibling 'estimate_cost' suggests cost context could be relevant.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise: two sentences with no wasted words. It front-loads the purpose and key behavior (queueing, immediate return, request_id).

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an async AI generation tool with no output schema and no annotations, the description is too brief. It fails to explain how to use the returned request_id (e.g., with status or result tools), what happens on failure, or any validation constraints. The complexity of the operation demands more context for a correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description adds no additional meaning beyond what the schema already provides for parameters like app_id, input_data, webhook_url, and output_format. The description's mention of 'request_id' concerns the return, not parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Submit' and the resource 'generation request to a fal.ai model', and explains that it queues the request and returns a request_id. However, it does not explicitly differentiate from sibling tools like 'search' or 'find', though the purpose is distinct enough.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus its siblings (e.g., cancel, status, result). It lacks prerequisites, alternatives, or context about the async workflow.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server