IA-QA — 152 QA & Dev Tools for AI Agents

list_llm_models

Read-onlyIdempotent

List all LLM models available on ia-qa.com with their provider, API endpoint, and capabilities. Filter by provider name (e.g. "Groq", "HuggingFace", "OpenAI") or return the full catalog. Use this to discover which models are available before calling an LLM API, or to compare providers.

Input Schema

Name	Required	Description	Default
`provider`	No	Filter by provider name (case-insensitive). E.g. "Groq", "HuggingFace", "OpenAI", "Anthropic", "Google", "DeepSeek", "xAI", "Ollama". Omit for full catalog.

Output Schema

Name	Required	Description	Default
`total`	No
`filter`	No
`models`	No
`providers`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 31, 2026
- addedInput schema / examples
```
Added value: +[
+  {
+    "provider": "groq"
+  }
+]
```

Changed1 schema field changedJun 21, 2026

addedOutput schema / properties

Added value: +{
+  "filter": {},
+  "models": {},
+  "providers": {},
+  "total": {
+    "type": "number"
+  }
+}

Changed1 schema field changedJun 10, 2026

changedOutput schema / (root)

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

First observedMay 26, 2026

TDQS

A3.9/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, and closed-world behavior, so the safety profile is covered. The description adds useful specifics beyond that: the catalog includes endpoints and capabilities, filtering is case-insensitive, and omitting the filter returns everything. It stops short of noting any rate limits or catalog freshness, which is acceptable for a simple read tool.

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?

Three compact sentences with the core purpose front-loaded, followed by the filtering behavior and then usage guidance. Minor redundancy between the description's provider examples and the schema's identical examples, but nothing is wasted.

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?

Complexity is low (one optional parameter, output schema present, rich annotations), and the description covers purpose, filtering behavior, and intended usage. The only gap is sibling disambiguation against model_info and compare_models, which an agent would still have to resolve on its own.

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% and the single provider parameter already carries a full description with an example list. The description largely restates that same information (provider examples, omit for full catalog) without adding syntax or matching behavior beyond 'case-insensitive', which the schema also states. Baseline 3 applies when the schema does the heavy lifting.

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 ('List all LLM models available on ia-qa.com') and enumerates what comes back: provider, API endpoint, and capabilities. It does not differentiate itself from potentially overlapping siblings such as model_info, llm_fit_finder, or compare_models, which keeps it out of 5 territory.

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 clear usage context: 'Use this to discover which models are available before calling an LLM API, or to compare providers.' It also states the filter-or-full-catalog choice explicitly. However, it names no alternatives or exclusions, so an agent choosing between this and model_info or compare_models gets no routing help.

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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