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

Annotations already declare readOnlyHint=true, and the description adds behavioral detail by enumerating exactly what the tool computes. It does not discuss output format or file size limits, but for a read-only analyzer these gaps are minor.

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 efficient sentence with the core operation front-loaded and no filler. The leading 'Image Quality Analyzer' phrase slightly duplicates the tool title/annotation, keeping it from a perfect score.

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 one-parameter, read-only analysis tool, the description sufficiently explains what the tool does and what metrics it returns. It omits the exact return format, but the listed metrics are enough for an agent to select and invoke the tool correctly.

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 100%: the only parameter, 'file', is already documented with accepted formats. The description's phrase 'of an image' adds little semantic value beyond the schema, so the baseline score of 3 applies.

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 uses a specific verb, 'Measure', and names four concrete outputs: sharpness, noise level, compression artefacts, and BRISQUE quality score. This clearly distinguishes it from sibling tools like analyze_image_similarity or analyze_metadata.

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 clear context: use this tool when you need objective image quality metrics. It does not explicitly mention alternatives or when-not-to-use conditions, so it stops just short of full routing guidance.

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