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FreeModel MCP

FreeModel MCP

by yummy342
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
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freemodel_recommend

Analyze any AI task to receive 2-3 model recommendations with reasons, using benchmark scores from 25+ platforms to match the optimal model for coding, reasoning, writing, and more.

Instructions

Recommend the best AI model for a given task. Analyzes your task and recommends 2-3 models with reasons.

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": {
      "task": "<task>"
    },
    "name": "freemodel_recommend"
  }
}

Input Schema

NameRequiredDescriptionDefault
taskYesTask description e.g. "写一个Python爬虫"

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4/5.0
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 discloses the core behavior (analyzes and recommends) and the output (2-3 models with reasons), but does not mention prerequisites (e.g., API key), potential side effects, or limitations. For a simple recommendation tool, this is adequate but not rich.

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 two sentences, front-loaded with the main purpose, and contains zero wasted words. Every word contributes to understanding what the tool does.

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?

The tool is simple (one required parameter), and the description explains the output sufficiently (2-3 models with reasons) without an output schema. It could mention prerequisites like a valid API key given sibling freemodel_key_health, but the description is otherwise self-contained and adequate.

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% with a clear example for the 'task' parameter, so the schema already handles the semantics. The description adds little beyond restating that the task is analyzed, fitting the baseline 3 for high schema 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 tool recommends the best AI model for a given task, analyzing the task and providing 2-3 models with reasons. This verb+resource combination is specific and distinct from sibling tools like freemodel_run (which runs models) and freemodel_models (which lists 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 implies usage when the user needs model recommendations for a task, providing clear context. However, it does not explicitly mention when not to use it or point to alternatives like freemodel_run for execution, so it stops short of a full 5.

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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