check_image_quality

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

No annotations are provided, so the description carries the burden. It explains the method ('uses texture analysis') and mentions reliability, but does not disclose side effects, auth requirements, rate limits, or error behaviors.

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?

Extremely concise (three short paragraphs), front-loaded with purpose, and no redundant information. Every sentence adds value.

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 simplicity (1 parameter, no nested objects, output schema exists), the description covers the core purpose and method. It lacks details on error handling or input validation, but the output schema likely supplements the return information.

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?

Only one parameter ('image_path') with 0% schema description coverage. The description adds 'Path to the image' but this is trivial and redundant with the schema title. No additional semantic value is provided.

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 explicitly states 'Analyze image quality: blur, contrast, texture complexity' which provides a clear verb and resource, and the listed aspects help distinguish it from sibling tools like 'analyze_image' or 'extract_features'.

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 includes a usage hint: 'More reliable than asking an LLM to visually judge blur', which implies when to use this tool over an alternative. However, it does not explicitly state when not to use it or directly compare to siblings.

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