IA-QA — 130+ QA & Dev Tools for AI Agents

model_info

Read-onlyIdempotent

Get detailed specs for an AI model: context window, pricing per 1K tokens, knowledge cutoff, provider, multimodal support, reasoning capabilities, and feature list. Covers 30+ models from OpenAI, Anthropic, Google, DeepSeek, Meta, Mistral, Cohere, xAI.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`model`	Yes	Model name (e.g. "gpt-4o", "claude-3.5-sonnet", "gemini-2.5-pro")

Output Schema

TableJSON Schema

Name	Required	Description	Default
`model`	No
`pricing_per_1k`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedJun 21, 2026

addedOutput schema / properties

Added value: +{
+  "model": {},
+  "pricing_per_1k": {
+    "type": "object"
+  }
+}

Changed1 schema field changedJun 10, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "type": "object"
+}

First observedMay 26, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds useful context about the specific data fields returned and the model coverage (30+ models, named providers), going beyond the annotation baseline.

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 sentences, front-loaded with purpose, then detailed field list and coverage. No wasted words; every sentence earns its place.

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 simple single-parameter lookup tool with a documented output schema and strong annotations, the description fully covers what the tool returns and its scope. No significant gaps.

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 100% with a clear parameter description and examples. The tool description does not add significant meaning beyond the schema, so the baseline of 3 is appropriate.

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 it retrieves detailed specs for an AI model, listing specific attributes (context window, pricing, knowledge cutoff, etc.) and provider coverage. This distinguishes it from siblings like list_llm_models or compare_models with a specific verb+resource.

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 use for single-model detail lookups, which is clear context. However, it does not explicitly mention alternatives or when not to use it, so it falls just short of a 5.

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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