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

With no annotations provided, the description must carry the full burden. It discloses the hash algorithms but does not explain input format (e.g., URL, file path, or base64), output structure, or potential side effects. The term 'q' is ambiguous, and the description adds minimal behavioral context beyond the algorithm names.

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 concise sentence with no filler. It packs the algorithm and purpose into a compact format, making it easy to parse quickly.

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?

Despite an output schema existing, the tool lacks essential context: input semantics, expected image source, and any practical usage notes. An agent would be unable to call this correctly without guessing what 'q' represents.

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 does not explain what the single 'q' parameter means. Without any guidance on how to provide the image to hash, an agent cannot correctly invoke the tool.

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 it computes an image hash using perceptual aHash plus sha256/md5 for duplicate matching. This is specific about the algorithm and use case, distinguishing it from siblings like imagephash (which likely does only perceptual hashing) and imagerev (reverse image search).

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 implies usage for duplicate matching but gives no explicit context on when to prefer this over alternatives like imagephash or imagerev. There are many sibling tools, and no guidance on selection criteria, input requirements, or exclusions.

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