ModelRadar MCP

modelradar_search

Search AI model catalogs by type, country, open weights, minimum context, or free-text query. Restrict results to OpenRouter-routable models for direct use.

Instructions

Search the ModelRadar catalog of AI models. Filter by type/country/open-weights/min context, or free-text query. Set only_routable=true to keep only models usable via OpenRouter.

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": "modelradar_search"
  }
}

Input Schema

Name	Required	Description
`type`	No
`limit`	No
`query`	No	free-text match on name/lab/description
`country`	No	e.g. US, CN, EU, JP
`min_context`	No	minimum context window in tokens
`open_weights`	No
`only_routable`	No	only models with an OpenRouter id

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 6, 2026

TDQS

A3.9/5.0

Behavior3/5

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

With no annotations, the description carries the full burden. It discloses the filtering behavior and the only_routable parameter's effect (keeping models usable via OpenRouter), but does not mention output format, pagination, or read-only nature. The behavior is partially transparent, not fully.

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?

Three short sentences, each adding distinct value: the action, the filter options, and the special flag. No fluff or repetition. The structure is front-loaded with the main purpose and then details.

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 7 parameters, no output schema, and no annotations, the description covers the main search and filter semantics but omits return format, limit behavior, and any prerequisites. It is adequate for a basic search tool but leaves gaps for an agent needing full operational details.

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 coverage is 57%, and the description adds meaning for type, country, open-weights, min context, and query by framing them as filters. However, it does not explain the 'limit' parameter, and the extra information is mostly redundant with existing parameter descriptions. It partially compensates for the missing descriptions but not fully.

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 identifies the tool as a search over the ModelRadar catalog, using a specific verb ('Search') and resource ('catalog of AI models'). It lists filter dimensions (type, country, open-weights, min context, free-text query) and the special 'only_routable' behavior, which distinguishes it from sibling tools like modelradar_get or modelradar_latest.

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: use this tool to search and filter the catalog. It provides clear context but does not explicitly state when not to use it or mention alternatives like modelradar_get or modelradar_recommend. The context is strong enough for an agent to infer when to invoke search.

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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