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thejaredchapman

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by thejaredchapman
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

list_models

Lists all providers and models in the catalog, including each provider's flagship model, reasoning support, and curated need/industry tags with explanations.

Instructions

List every provider and model in the catalog, plus each provider's frontier (flagship) model.

Each model carries a `reasoning` flag (supports extended reasoning) and a `tags` list of
curated need/industry tag ids; `tags` in the result gives their labels, one-line "why"
explanations and the date they were verified. Tags are a starting point, not benchmarks.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose the returned shape's content: a reasoning flag, tag ids, and tag labels with 'why' explanations and verification dates. It omits any mention of auth, pagination, ordering, or size limits, so the disclosure is partial.

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?

The core purpose is front-loaded in the first sentence, and the second block explains returned fields with little filler. It is slightly over-explained for a no-argument list tool but not wasteful.

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

Completeness3/5

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

With no output schema, the description usefully documents the shape of results, covering the main completeness gap. What remains missing is routing guidance against sibling tools like suggest_models, which an agent needs to pick correctly in this catalog-oriented toolset.

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?

The tool takes zero parameters, so there is nothing to document and the baseline of 4 applies. The description correctly avoids inventing parameter semantics.

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?

The description states a specific verb and resource ('List every provider and model in the catalog') and adds scope detail (frontier flagship model per provider). It does not, however, explicitly contrast itself with the sibling suggest_models, so an agent must infer the difference.

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

Usage Guidelines2/5

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

There is no explicit statement of when to use this tool versus suggest_models or list_availability. The line 'Tags are a starting point, not benchmarks' faintly hints that this is a browse-first tool, but no alternative is named and no conditions are given.

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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