post-generate-custom

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

No annotations are provided, so the description carries the full burden. It discloses that the tool is for generation (implying a write operation) and notes availability constraints for certain models, which is useful behavioral context. However, it lacks details on permissions, rate limits, or what the generation entails (e.g., whether it's async, returns a job ID). The description doesn't contradict annotations, as there are none.

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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by essential notes and alternatives. It avoids unnecessary fluff, though the note about model availability could be more concise. Every sentence adds value, making it efficient overall.

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?

Given the tool's complexity (generation with custom models), lack of annotations, no output schema, and incomplete parameter documentation, the description is moderately complete. It covers key usage scenarios and constraints but misses details on behavioral aspects like error handling or response format. It's adequate but has clear gaps for a tool with 3 parameters and significant functionality.

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 33% (only the 'body' parameter has a description). The description adds meaning by explaining that the 'body' must be retrieved from 'GET /models/{modelId}' inputs fields, which clarifies its purpose beyond the schema. However, it doesn't address 'dryRun' or 'modelId' parameters, leaving gaps. With low schema coverage, the description partially compensates but not fully.

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's purpose: 'Generate with any model (Image, Video, Audio, 3d).' It specifies the action (generate) and resource types (models for various media). However, it doesn't explicitly differentiate from sibling tools like 'post-txt2img-inferences' or 'post-img2img-inferences' that handle specific model types, though it implies a broader scope.

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?

The description provides explicit usage guidelines: it tells when to use this tool (for any model generation) and when not to use it (for SD1.5, SDXL, Flux.1 and Flux.1-Kontext based models, where alternative endpoints like 'POST /generate/txt2img' should be used). It also mentions retrieving model inputs from another endpoint, adding context for prerequisites.

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