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

With no annotations provided, the description does carry the burden of disclosing behavior. It explains the specific checks performed (robots.txt, llms.txt, /.well-known/ai, structured data) and the two modes (single station vs. industry summary). However, it omits potential outcomes like error behavior for unknown callsigns, rate limits, or return format, which prevents a higher 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 a single dense sentence that packs in a lot of information, but its structure is slightly confusing: the list of checks is appropriate, yet the parameter instructions ('Pass a callsign') are vague and not front-loaded with a clear hierarchy. It could be split into two sentences for better readability, and the parameter guidance is insufficient.

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 no annotations, no output schema, and two parameters (one undocumented), the description is incomplete. It explains what data is gathered but not how results are returned (format, structure), how to interpret the summary, or how errors are handled. Combined with the parameter ambiguity, an agent would struggle to invoke it correctly without more 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?

Schema description coverage is only 50% (query has a description, callsign does not). The tool description says 'Pass a callsign' but does not clarify whether to use the 'query' or 'callsign' parameter, and the free-text option for 'query' is not reconciled with the callsign field. This ambiguity adds no value beyond the schema and fails to compensate for the missing callsign description.

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 the tool's purpose: determining whether a US TV station website is readable by AI assistants, with specific checks (robots.txt, llms.txt, structured data). It is specific and informative, though it doesn't explicitly differentiate from sibling tools like media_closed or media_verify, so a perfect score is not warranted.

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 gives input guidance (pass a callsign or nothing for summary) but no guidance on when to choose this tool over the many siblings in the media_* family. There is no comparison or alternative mentioned, leaving selection to inference. This is a significant gap.

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