picorn_generate_imageGenerate a raw image with self-hosted Z-Image Turbo. Returns an inline preview and public creationUrl.
| Name | Required | Description | Default |
|---|---|---|---|
| seed | No | ||
| width | No | ||
| height | No | ||
| prompt | Yes | ||
| preview | No | ||
| aspectRatio | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds context beyond annotations by noting it returns an inline preview and a public creationUrl, indicating the generated image is publicly accessible. However, it doesn't disclose potential costs, rate limits, or what happens to the image after generation. It does not contradict annotations (readOnlyHint=false, openWorldHint=true).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the main action, and contains no redundant words. It efficiently conveys the core purpose and key output details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With six parameters, no output schema, and no parameter explanations, the description leaves significant gaps. It mentions the return includes a preview and creationUrl but doesn't explain parameter usage or output structure. The tool is a complex generation action, so more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description provides no explanation for any of the six parameters (prompt, seed, width, height, preview, aspectRatio). Without any parameter guidance, the agent cannot infer how to set values correctly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates a raw image using Z-Image Turbo, which is a specific verb and resource. However, it does not differentiate from sibling tools like picorn_design_image or picorn_edit_image, which also generate or modify images.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool instead of alternatives. There is no mention of use cases, exclusions, or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct resource or operation, with clear boundaries between image generation/editing and the many editor/video actions. Descriptions further clarify any potential overlap, making misselection unlikely.
Most tools follow a verb_noun pattern, but the projects_* tools (create, inspect, update) reverse to noun_verb, and capabilities is a bare noun. This mixed convention is readable but not fully consistent.
With 30 tools, the set exceeds the 25-tool threshold and feels heavy for an MCP server. While the broad scope justifies some size, many tools could be consolidated or split into focused servers.
Core image, editor, and video workflows are covered, but there are notable gaps such as no delete operations for editor/video projects, no update for video projects, and no lifecycle management for imported assets. These missing operations can create dead ends.