Skip to main content
Glama

List Image Models (OpenRouter)

pixara_list_image_models
Read-onlyIdempotent

Browse and filter OpenRouter's image-generation models by provider, capability (e.g., transparent backgrounds), and pagination to discover model IDs before generating or editing images.

Instructions

List image-generation models available on OpenRouter, with filtering and pagination.

This is a read-only, no-cost call (no image is generated). Use it to discover model IDs before calling pixara_generate_image or pixara_edit_image, or to find models with a specific capability.

Args:

  • filter (string, optional): Case-insensitive substring match against model id or name.

  • provider (string, optional): Restrict to models whose id starts with this prefix, e.g. 'openai', 'black-forest-labs'.

  • supports_img2img (boolean, optional): Only models that accept input_references.

  • supports_streaming (boolean, optional): Only models that support SSE streaming.

  • supports_transparent_background (boolean, optional): Only models supporting transparent backgrounds.

  • limit (number, 1-100, default 30), offset (number, default 0): Pagination.

  • response_format ('markdown'|'json', default 'markdown').

Returns: For JSON: { total, count, offset, models: [...], has_more, next_offset? } For Markdown: a heading per model with id, description, and supported parameter names.

Examples:

  • Use when: "What image models can do transparent backgrounds?" -> supports_transparent_background=true

  • Use when: "Show me FLUX models" -> filter='flux'

  • Don't use when: you already know the exact model id (skip straight to generate_image)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of models to return (1-100).
filterNoCase-insensitive substring match against model id or name.
offsetNoNumber of models to skip, for pagination.
providerNoFilter to models whose id starts with this provider prefix, e.g. 'openai'.
response_formatNoOutput format: 'markdown' for humans or 'json' for machine processing.markdown
supports_img2imgNoOnly include models that support image-to-image via input_references.
supports_streamingNoOnly include models that support SSE streaming.
supports_transparent_backgroundNoOnly include models that support transparent backgrounds.
Behavior4/5

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds that it is a no-cost call (no image generated), providing useful behavioral context beyond the annotations.

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 description is well-structured with clear sections (Args, Returns, Examples) and front-loaded key points. Though somewhat long, every sentence earns its place.

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?

Given 8 parameters and no output schema, the description adequately covers return formats for both JSON and Markdown. It provides sufficient context for a discovery tool, though more detail on pagination behavior could be added.

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?

Schema coverage is 100%, giving a baseline of 3. The description adds semantic meaning by explaining each filter's purpose (e.g., case-insensitive substring match) and output format behavior, enhancing understanding.

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 lists image-generation models with filtering and pagination. It distinguishes itself from siblings by specifying it is a read-only discovery call before using pixara_generate_image or pixara_edit_image.

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

Usage Guidelines5/5

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

Explicit when-to-use and when-not-to-use examples are provided, such as 'Use when: What image models can do transparent backgrounds?' and 'Don't use when you already know the exact model ID.' It also references sibling tools.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/pinkpixel-dev/pixara-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server