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fal

fal

by derekalia
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

search

Find AI models on fal.ai using keywords to match your project needs. Returns relevant models based on search criteria.

Instructions

    Search for models on fal.ai based on keywords.
    
    Args:
        keywords: The search terms to find models
        
    Returns:
        A list of models matching the search criteria

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": {
      "keywords": "<keywords>"
    },
    "name": "search"
  }
}

Input Schema

NameRequiredDescriptionDefault
keywordsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool searches and returns a list, but lacks details on permissions, rate limits, pagination, error handling, or whether it's read-only/destructive. For a search tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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

Conciseness4/5

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

The description is appropriately sized and front-loaded, with the core purpose stated first in a clear sentence. The Args and Returns sections are structured but slightly verbose for a single parameter; every sentence earns its place by providing essential information without waste.

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?

Given the tool's low complexity (one parameter, no annotations, no output schema), the description is complete enough for basic understanding. It covers what the tool does, the parameter, and the return type. However, it lacks context on behavioral traits and usage guidelines, making it adequate but with clear gaps for effective agent 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?

The description adds meaning beyond the input schema by explaining that 'keywords' are 'search terms to find models,' which clarifies the parameter's purpose. However, with 0% schema description coverage and only one parameter, the baseline is high, and it doesn't compensate with details like format examples or constraints. The value added is minimal but adequate.

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 tool's purpose: 'Search for models on fal.ai based on keywords.' It specifies the verb ('search'), resource ('models'), and platform ('fal.ai'), making the intent unambiguous. However, it doesn't differentiate from sibling tools like 'models' or 'schema', which might also involve model-related operations.

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 alternatives. It doesn't mention sibling tools like 'models' (which might list models without searching) or 'generate' (which might use models), nor does it specify prerequisites or exclusions. Usage is implied only by the action of searching with keywords.

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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