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

Annotations already cover safety (readOnlyHint=true, openWorldHint=false), and the description adds substantive context beyond them: substring matching, most-transcribed-first ranking, a hard cap of 20 results, and no auth/key requirement with no cost. It omits matching case-sensitivity and whether truncated result sets can be paged.

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?

Three short sentences, front-loaded with what the tool returns and why it matters, then cost/auth, then the result cap. Nothing is repeated from the schema or annotations.

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?

With no output schema, the description does the right work by naming the returned slug, the sort order, and the 20-result ceiling. Remaining gaps are minor: no statement on empty/no-match results or on how to retrieve more than 20 matches.

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 coverage is 0%, so the description carries the burden, and it does explain that `name` is a substring match rather than an exact title. It does not address the schema's 2–100 length bounds or case sensitivity, but the core semantics are covered for a single required parameter.

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?

Names the resource (public podcasts), the matching rule (name contains the text), and the ordering, so the agent knows exactly what it retrieves. It also implicitly separates itself from search_transcripts by pointing at that tool as the consumer of the slug. No ambiguity with any sibling.

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?

"with their slug (for the podcast filter of search_transcripts)" makes the intended workflow explicit: resolve a podcast here, then filter transcripts there. It does not state exclusions (e.g. when to skip this and query transcripts or ask_question directly), so it falls short of a full when/when-not rubric.

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