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Search Relay models

search_models
Read-onlyIdempotent

Find AI models by filtering tags like text-to-image or video-to-video and text queries. Returns name, tag, and route for each model matching the criteria.

Instructions

Lists Relay models by tag and/or text. Common tags: text-to-image, image-to-image, text-to-video, image-to-video, video-to-video, text-to-speech, speech-to-text, text-to-text, text-to-embedding. image-to-image also lists every image model that takes an input image. Returns name, tag, route and accepts (input media the model takes, e.g. ["image"]; [] for none) per model (at most 50). With a credential the list is your account's view (what its tier and grants can run), public otherwise. Then call get_model with a name for its input schema.

Input Schema

NameRequiredDescriptionDefault
tagNoModel type tag, e.g. text-to-image or image-to-video. Omit for every tag.
queryNoWords to match, case-insensitive; every word must appear in the name, display name or tag (e.g. 'kling v3').

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.8

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/openWorld, yet the description still adds real value: a hard result cap ("at most 50"), the fact that visibility differs with and without a credential, and the exact per-model return fields. It omits pagination/truncation behavior and says nothing about rate limits.

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?

Front-loaded with purpose, then capabilities, then return shape, then the next step. The tag enumeration is long but earns its space as the only source of that vocabulary; a couple of clauses could still be tightened.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-param read tool with no output schema, it supplies return fields, the result cap, the auth-dependent scoping, and the next call to make. An agent has everything needed to invoke it and act on results.

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?

Schema coverage is 100%, so the baseline is 3; the description goes beyond it by enumerating the valid tag vocabulary (absent from the schema's enums) and by clarifying that image-to-image deliberately widens to all image-in models. The query semantics are already in the schema, so the gain is partial rather than decisive.

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?

Opens with a specific verb and resource ("Lists Relay models by tag and/or text") and explicitly separates itself from get_model, which it names as the follow-up for input schemas. An agent can distinguish it from run_model or get_pricing without opening any schema.

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?

Gives a clear two-step flow ("Then call get_model with a name for its input schema") and explains the credential-dependent view. It stops short of naming exclusions against other siblings such as get_pricing or list_workflows, but the usage context is unambiguous.

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