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FreeModel MCP

FreeModel MCP

by yummy342
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Local

freemodel_models

See every AI model available across platforms. Returns platform and model names, so you can select the right one for your needs.

Instructions

List all AI models available to you across platforms. Returns platform and model names.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "freemodel_models"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, so the description must disclose behavior. It states the operation is a listing and describes the return content, which is adequate for a simple read-only tool. However, it does not mention error conditions, authentication, or any operational constraints, leaving some behavioral burden unmet.

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 a single, front-loaded sentence with no unnecessary words. Every piece of information earns its place: the action, the scope, and the return content.

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 simplicity (no parameters, no output schema), the description adequately covers the main context: what it lists and what it returns. It lacks minor details like potential size or pagination, but these are not critical for a basic listing tool.

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 tool has zero parameters, and the baseline for 0 params is 4. The description adds valid context by specifying the output fields, which is sufficient given no parameters exist to document.

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 a specific verb 'List' with a resource 'all AI models available to you across platforms' and explicitly notes the return content ('platform and model names'). This distinguishes it from sibling tools like freemodel_key_health and freemodel_run, which serve different purposes.

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 the siblings. It does not mention alternatives, prerequisites, or exclusions, leaving the agent to infer usage solely from the tool's name and basic description.

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

Deploy Server

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