fal.ai MCP Server

find

Retrieve specific AI models by their endpoint IDs. Useful for looking up exact models using stable identifiers.

Instructions

Find specific model(s) by endpoint ID. Can retrieve single or multiple models (1-50). Useful for looking up exact models by their stable identifiers.

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": {
      "endpoint_ids": "<endpoint_ids>"
    },
    "name": "find"
  }
}

Input Schema

Name	Required	Description	Default
`endpoint_ids`	Yes	Endpoint ID(s) to retrieve (e.g., ['fal-ai/flux/dev', 'fal-ai/flux-pro']). Can specify 1-50 models.
`expand`	No	Fields to expand in response. Supported: 'openapi-3.0' (includes full OpenAPI schema)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.1.4Jun 15, 2026

TDQS

A4.1/5.0

Behavior3/5

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

With no annotations, the description covers the retrieval nature and batch size (1-50), but lacks details on error handling, authorization requirements, or rate limits. It's adequate for a simple read tool but not comprehensive.

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?

Two sentences with no extraneous information; the key purpose and constraint are presented upfront, achieving high efficiency.

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

Completeness3/5

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

Without an output schema, the description does not explain the response structure (e.g., model details, pagination). For a retrieval tool, this leaves a gap in completeness, though siblings like 'search' might offer more context.

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?

The schema covers both parameters fully (100% coverage), but the description adds value by specifying the allowed range (1-50) for endpoint_ids and providing an example format, going beyond the schema alone.

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 tool retrieves models by endpoint ID, distinguishes from fuzzy search by emphasizing 'exact models' and 'stable identifiers', and specifies the range of 1-50 models.

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

Usage Guidelines4/5

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

The description indicates use for exact lookup via endpoint IDs ('Useful for looking up exact models'), but does not explicitly contrast with sibling tools like 'search' or 'models' for alternative usage.

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

Deploy Server