universal-image-mcp

list_models

List available image generation models from all enabled providers, returning model IDs for use with generate_image and transform_image. Models are fetched dynamically from each provider's API.

Instructions

List available image generation models from all enabled providers.

Returns a formatted list of model IDs that can be used with generate_image and transform_image. Models are fetched dynamically from each provider's API.

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

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.3Jul 31, 2026

TDQS

A4.2/5.0

Behavior3/5

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

With no annotations, the description carries full responsibility for transparency. It discloses that models are fetched dynamically from each provider's API, indicating a live network operation. However, it does not explicitly state that it is read-only, potential latencies, or failure modes if providers are unavailable. This is adequate but not comprehensive.

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?

The description is concise with exactly three sentences, each serving a purpose: stating the core function, describing the output and its use, and noting the dynamic data source. No extraneous content or repetition.

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?

For a simple no-parameter listing tool, the description covers the essentials: what it lists, the output format (formatted list of IDs), and how data is obtained. Given an output schema exists, it doesn't need to detail the return structure. Minor gaps include potential error behavior and network requirements, but these are not critical for this simple tool.

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 has zero parameters, so the schema coverage is trivially 100% and the description needs to add no parameter details. Baseline for 0 params is 4, and the description confirms no arguments are needed by focusing entirely on the listing operation.

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?

The description clearly states the tool's function with a specific verb ('List') and resource ('available image generation models from all enabled providers'). It distinguishes itself from sibling tools by explicitly noting the output (model IDs) and its relationship to generate_image and transform_image.

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?

The description implies the primary use case: retrieve model IDs before using generate_image or transform_image. It provides clear context but does not explicitly state 'use this before other tools' or mention when not to use it. The linkage to sibling tools effectively guides usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server