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search_models

Search the model register: open-weight AI model releases with exact parameter counts, architectural context length, licence and release date. Use this to answer questions about which models exist, how big they are, what they are licensed under, or what shipped recently.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskNoe.g. text-generation, image-text-to-text
limitNoDefault 25, max 100
queryNoSubstring match on the model id
licenseNoe.g. apache-2.0, mit
reasoningNoOnly models whose chat template shows reasoning/thinking support
image_inputNoOnly models that accept image input
min_contextNoMinimum architectural context length in tokens
organisationNoe.g. qwen, deepseek-ai, meta-llama, google
tool_callingNoOnly models whose chat template shows tool-calling support
max_parametersNo
min_parametersNoMinimum parameter count, e.g. 7000000000
released_afterNoISO date, e.g. 2026-01-01
include_derivativesNoInclude quantisations and conversions of other releases (GGUF, MLX, AWQ and similar). Default false.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior3/5

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

No annotations exist, so the description must carry the behavioral load; it establishes this as a read-only search over a curated register and describes what each result record contains. It omits result ordering, pagination behavior, and any constraints that would matter with 13 filter parameters.

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?

Two dense sentences with the scope and returned fields front-loaded before the use cases. Every clause carries information, though the second sentence's 'what shipped recently' clause is wasted because it duplicates a sibling.

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?

For a 13-parameter filter tool with no output schema and no annotations, the description adequately conveys domain, scope, and returned fields. Gaps remain around result ordering and sibling routing, but nothing essential to invoking it correctly is missing.

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 92%, so the schema already documents nearly every parameter and the 3 baseline applies. The description loosely maps to a few filters (exact parameter counts, context length, licence, release date) and adds the 'open-weight' scope, but supplies no syntax, units, or behavior beyond the schema.

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?

States a specific verb ('Search') and resource ('the model register') and enumerates the record content (parameter counts, context length, licence, release date). It is easy to distinguish from get_model or register_stats, but it never names a sibling explicitly and 'what shipped recently' overlaps with recent_releases.

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

Usage Guidelines3/5

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

'Use this to answer questions about which models exist, how big they are, what they are licensed under...' gives concrete use contexts, which is better than nothing. However, there is no when-not guidance, and it does not address that recent_releases is the more direct sibling for the 'what shipped recently' case it claims.

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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