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There's An AI For That

Get model details

get_model
Read-only

Get public details for one AI model by slug. Preserve returned URLs exactly.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesThe model slug returned by search_models.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds two real behavioral facts beyond that: only public details are returned (scope limitation) and returned URLs must be preserved exactly (fidelity constraint for downstream use).

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?

Two short sentences, no filler. The core operation leads and the URL-preservation caveat follows; both carry information an agent needs.

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?

An output schema exists so return values need not be explained, and the annotations cover safety. The description supplies the scope caveat ('public details') and the URL handling instruction, leaving little an agent needs missing; only an explicit sibling routing statement is absent.

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?

Single parameter with 100% schema description coverage, so the schema fully documents 'slug' as the value returned by search_models. The description adds no format, casing, or sourcing detail beyond the schema, so baseline 3 applies.

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 and resource ('Get public details for one AI model') plus the keying mechanism ('by slug'), which cleanly separates it from the search_* siblings. It does not explicitly name an alternative like search_models, so it falls just short of the top band.

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?

The 'by slug' phrasing implies the prerequisite (you must have a slug, which the schema says comes from search_models), but the description itself never states when to use this versus search_models or get_company-style siblings. Usage is inferable rather than stated, which is the definition of minimum viable.

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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