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skelly-77

openrouter-image-mcp

by skelly-77
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

generate_image

Create images from text prompts with OpenRouter image models; options are validated before billing, and outputs save with settings and cost.

Instructions

Generate new images from a text prompt with an OpenRouter image model.

Call list_image_models first to choose a model, and get_image_model for its exact allowed values. Options are checked against the model before anything is sent: an invalid value fails with the allowed values and costs nothing.

Each call costs money on the user's OpenRouter account; the result reports the cost. Prompts go to a third-party model provider through OpenRouter: get the user's OK before sending client or project images or details to third-party providers.

Images are saved (never overwriting) to the configured output folder with a JSON sidecar of the settings used. The result lists the saved paths, any notes and failures, the model, provider, time and cost, plus JPEG previews.

Args: prompt: What to create. model: Model id from list_image_models. n: Number of images, 1 to 10. Above the model's max n, the server splits them into several calls (each billed) and says so in the notes. aspect_ratio: One of the model's aspect ratios, e.g. "16:9". resolution: One of the model's resolution tiers. size: Exact pixel size such as "1024x1024", for models that support it. quality: One of the model's quality values. seed: Integer for repeatable results, for models that support it. background: One of the model's background values, e.g. "transparent". output_format: One of the model's output formats, e.g. "png" or "svg". output_dir: Folder to save into instead of the default. Must be an absolute path or start with "~". filename_prefix: File name stem; defaults to a slug of the prompt. provider_options: Provider passthrough options as a flat dict, e.g. {"moderation": "low"}. Keys must be in the model's passthrough parameters (see get_image_model); the server sends each key to every provider that allows it. Any other key is rejected before anything is spent.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nNo
seedNo
sizeNo
modelYes
promptYes
qualityNo
backgroundNo
output_dirNo
resolutionNo
aspect_ratioNo
output_formatNo
filename_prefixNo
provider_optionsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so: it discloses that validation happens before any spend ('an invalid value fails with the allowed values and costs nothing'), that each call is billed to the user's account, that n above the model max splits into multiple billed calls, and that files are saved never overwriting plus a JSON sidecar. It also describes the return payload in detail.

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 purpose, the pre-call workflow and the cost/consent warnings are front-loaded, which is the right ordering. The Args block is long, but each line carries non-redundant semantics for a 13-parameter tool with zero schema coverage; only slight tightening is possible.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema exists, yet the description enumerates the result contents (saved paths, notes and failures, model, provider, time, cost, JPEG previews), covers the cost/consent/privacy dimensions, and routes to the two prerequisite lookup tools. Nothing an agent needs to call this correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate entirely, and it documents all 13 parameters with meaning beyond the schema: n's 1-10 range and server-side splitting behavior, output_dir's absolute-or-tilde path rule, filename_prefix defaulting to a prompt slug, and provider_options' passthrough validation rules. This is far more than the bare schema provides.

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 ('Generate new images from a text prompt') plus the backend ('OpenRouter image model'), which cleanly distinguishes it from siblings edit_image, list_image_models and get_image_model. An agent can select it without opening the schema.

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?

Explicitly prescribes the workflow ('Call list_image_models first... and get_image_model for its exact allowed values') and the human-in-the-loop condition, plus a concrete when-not ('get the user's OK before sending client or project images or details to third-party providers'). Nothing is left to inference.

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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