post-detect-inferences

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'transforming visual data' which implies a processing operation, but doesn't specify whether this is read-only or mutating, what the output format is, performance characteristics, error conditions, or authentication needs. The description is too high-level to provide meaningful behavioral context for safe invocation.

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 a single sentence that efficiently states the tool's purpose. It's appropriately sized for what it communicates, though it could be more informative. There's no wasted verbiage or unnecessary elaboration, making it structurally sound if minimal.

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?

For a tool with 11 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what 'mode maps' are, what the transformation produces, error handling, or practical use cases. Given the complexity implied by multiple modality options and numerous parameters, the description should provide more context about the tool's behavior and output.

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 91%, so the schema already documents most parameters well. The description adds no additional parameter information beyond what's in the schema - it doesn't explain how parameters interact, provide examples, or clarify edge cases. With high schema coverage, the baseline score of 3 is appropriate since the description doesn't add value but also doesn't detract from the schema's documentation.

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 states the tool transforms visual data into mode maps for advanced precision in image generation, which gives a general purpose. However, it's vague about what 'mode maps' are and doesn't clearly distinguish this from sibling tools like 'post-segment-inferences' or 'post-controlnet-inferences' that also process images. The description lacks specificity about the exact transformation being performed.

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 explicit guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, ideal use cases, or contrast with sibling tools like 'post-caption-inferences' or 'post-describe-style-inferences'. Usage is implied through the mention of 'image generation' and 'mode maps', but no concrete scenarios or exclusions are stated.

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