fal-mcp

raw_generate

Submit custom requests to fal.ai models for advanced configurations like ControlNet, IP-Adapter, multi-LoRA, and custom parameters not covered by standard tools.

Instructions

Submit an arbitrary request to any fal.ai model endpoint.

Use this for advanced configurations (ControlNet, IP-Adapter, multi-LoRA, custom parameters) that aren't covered by the other tools.

Args: model: The fal.ai model endpoint ID (e.g. "fal-ai/flux-general"). body: The full JSON request body to send to the model.

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": {
      "model": "<model>",
      "body": "<body>"
    },
    "name": "raw_generate"
  }
}

Input Schema

Name	Required	Description	Default
`model`	Yes
`body`	Yes
`filename`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Feb 20, 2026

TDQS

A4.4/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It mentions the tool submits requests to external endpoints, implying network calls and potential latency/errors, but lacks details on authentication needs, rate limits, or error handling. It adds some context about advanced use cases but doesn't fully cover behavioral traits.

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 front-loaded with the core purpose, followed by usage guidelines and parameter explanations in a structured 'Args:' section. Every sentence adds value without redundancy, making it efficiently sized and well-organized.

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

Completeness4/5

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

Given the tool's complexity (3 parameters, nested objects, 0% schema coverage) and the presence of an output schema, the description is mostly complete. It covers purpose, usage, and key parameters but could improve by mentioning the optional 'filename' parameter or behavioral aspects like error handling, though the output schema mitigates some gaps.

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

Parameters4/5

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

Schema description coverage is 0%, so the description must compensate. It explains the 'model' parameter as the endpoint ID with an example and 'body' as the full JSON request, adding meaning beyond the schema. However, it omits the optional 'filename' parameter, leaving a minor gap in coverage.

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

Purpose5/5

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

The description clearly states the specific action ('Submit an arbitrary request') and resource ('any fal.ai model endpoint'), distinguishing it from sibling tools by mentioning advanced configurations not covered by other tools like ControlNet, IP-Adapter, multi-LoRA, and custom parameters.

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

Usage Guidelines5/5

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

Explicitly states when to use this tool ('for advanced configurations... that aren't covered by the other tools') and implies when not to use it (for simpler cases handled by siblings like edit_image, generate_image, etc.), providing clear context and alternatives.

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

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