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Codex Image Generation MCP

Codex Image Generation MCP

by TangRj-Git
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

List Models

list_models
Read-onlyIdempotent

Lists provider dialogue models, ranks them by quality or speed, and recommends a documented image-tool caller; excludes image, audio, and embedding models.

Instructions

List provider dialogue models and recommend a documented image-tool caller.

Does not generate images or verify paid tool execution. Image/audio/embedding models are excluded; unclassified provider aliases are returned separately.

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
refreshNoRefresh the provider list and official capability catalog instead of using fresh caches.
model_policyNoRank dialogue models for capability or speed. Default: configured policy, normally quality.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

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, open-world and non-destructive, so the safety profile is covered. The description adds value beyond that by disclosing that the recommendation is not a verification of paid tool execution and that unclassified provider aliases come back in a separate bucket, both of which shape how an agent should interpret results.

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?

Two compact sentences with the primary purpose front-loaded and exclusions following. The phrasing 'recommend a documented image-tool caller' is dense and takes a moment to parse, but no sentence 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?

There is no output schema, so the description carries the burden of explaining returns; it does so partially by indicating a dialogue-model list, a separate unclassified-aliases bucket, and a recommendation. It stops short of describing ordering, fields, or how the model_policy ranking is surfaced, but covers enough for correct invocation.

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 description coverage is 100% and both parameters (refresh, model_policy) are already documented with defaults and enum values in the schema. The description adds no syntax, format, or interaction detail for either parameter, so the 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?

The description uses a specific verb plus resource ('List provider dialogue models') and immediately scopes the result by excluding image, audio and embedding models, which separates it from the sibling list_image_models and from generate_image. The second clause ('recommend a documented image-tool caller') adds a secondary purpose that slightly blurs what the tool fundamentally returns, keeping it short of a clean 5.

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?

It supplies explicit exclusions: no image generation, no paid tool execution verification, and image/audio/embedding models are not included — which implicitly routes the agent to list_image_models and generate_image. It never names those siblings directly, so the alternative-selection guidance is inferential rather than stated.

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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