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

With no annotations, the description carries the full burden of behavioral disclosure. It clearly implies a read-only mapping operation ('Map... to curated metadata') and limits its scope by excluding byte sniffing, encoding, and DNS lookups. However, it does not mention error handling for unknown extensions or case sensitivity, which prevents a perfect score.

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 efficiently structured: a concise overview sentence, followed by bullet-style 'Use when' examples and a clear 'Do not use when' list. Every sentence contributes directly to decision-making, with no redundancies or filler. The front-loaded purpose sentence ensures immediate clarity.

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 low-complexity tool with one parameter, no annotations, and no output schema, the description covers all necessary decision points: what it does, when to use it, when not to use it, and what the query parameter accepts. The exclusions and alternatives provide a complete context for selection, making it sufficient for an AI agent to invoke 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?

The input schema only defines 'query' as a non-empty string with zero description. The description compensates by specifying that the query accepts either a file extension (e.g., 'webp', 'png') or a MIME type string (e.g., 'application/json'), with concrete examples. This adds meaning beyond the schema, though it doesn't detail formatting edge cases like leading dots or case handling.

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 first sentence clearly defines the tool: 'Map a file extension such as webp or png, or a MIME type string, to curated Content-Type metadata.' This is a specific verb+resource action that distinguishes it from sibling lookups like tld_lookup or currency_lookup. The description also states concrete use cases (setting headers, validating uploads, choosing media types), making the purpose unmistakable.

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 an explicit 'Use when:' section with three example queries and a 'Do not use when:' section with three exclusions, naming alternative tools (base64_encode/decode, tld_lookup). This gives the agent clear decision criteria for when to invoke this tool versus siblings, exceeding the minimum requirement.

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