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Polydev

Polydev

Official
by polydev-ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Hybrid

List models

list_models
Read-only

Retrieve the AI models you can access now and see what pays for each—ChatGPT plan, local Claude Code/Codex, or Polydev—so you can choose the right model.

Instructions

List the models this user can reach right now and what pays for each: the ChatGPT plan, Claude Code and Codex on this machine, and models through Polydev.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_models"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

The annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=true, so the safety profile is covered. The description adds that availability is live ('right now') and that each result carries a funding source (ChatGPT plan, local Claude Code/Codex, Polydev) — useful context — but says nothing about latency, partial failures, or ordering for a probe that spans several backends.

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?

A single front-loaded sentence with the action first and the source breakdown in a colon list. Every clause carries information; nothing is redundant.

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?

With no output schema, the description does part of the return-value work by explaining that results pair models with what pays for them. Combined with annotations covering safety, an agent has enough to call it correctly, though the shape of the response and any freshness/caching caveats are left unstated.

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 and the schema is fully covered, so there is nothing for the description to disambiguate. Baseline 4 applies for a no-parameter tool.

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 the models') plus the scope qualifier 'this user can reach right now', which separates it from ask_model (invoking a model) and chatgpt_plan_status (plan-level only). The purpose is clear, though sibling routing is implied by scope rather than stated explicitly.

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?

There is no explicit when-to-use or when-not-to-use guidance, and no alternative is named. The 'right now' framing implies it is a discovery step before invoking a model, but the agent must infer that from the description rather than being told.

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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