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Codex Image Generation MCP

Codex Image Generation MCP

by TangRj-Git
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

List Image Models

list_image_models
Read-onlyIdempotent

Lists official OpenAI image models from documentation without querying provider /models, so you can identify supported models without credentials. Stale or fallback catalogs are labeled.

Instructions

List image models from official OpenAI docs, independently of provider /models.

Requires no provider credentials. Results describe official support, not verified availability through a relay. Stale or bundled fallback catalogs are labeled.

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_image_models"
  }
}

Input Schema

NameRequiredDescriptionDefault
refreshNoRefresh public official docs instead of using the daily cache.
model_policyNoRecommend for capability or speed. Default: CODEX_IMAGE_MODEL_POLICY, normally quality.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare the safe, read-only, idempotent, open-world profile, and the description adds genuinely non-derivable facts: no provider credentials required, a daily cache with implications for freshness, and that stale or bundled fallback catalogs are labeled. Only the return shape and recommendation behavior are left implicit.

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?

Three short sentences, front-loaded with the core action and scope, followed by the two caveats that matter (credential independence and catalog staleness). No padding or repetition of the title.

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 and only two optional flags, the definition covers the operational facts an agent needs: no credentials, caching, and labeled stale catalogs. It could say more about what a returned model entry or recommendation contains, but nothing essential for correct invocation is missing.

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%, so both optional parameters (refresh, model_policy) are already documented in the schema. The description adds no parameter-level meaning such as what a policy recommendation changes in the output, so it meets only the baseline.

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?

States a specific verb and resource ('List image models') plus the source ('official OpenAI docs') and explicitly separates itself from the sibling-provider listing ('independently of provider /models'). An agent can distinguish this from list_models without opening either schema.

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?

Makes clear when this tool applies (official support information that should not depend on provider credentials) and implicitly routes availability questions to the provider-level sibling. It stops short of an explicit 'use X instead when Y' exclusion, so it is strong context rather than full when/when-not routing.

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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