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

Annotations already declare readOnly=true, idempotent=true, openWorld=true, so the safety profile is covered; the description adds meaningful behavior by disclosing the corpus scope (3,800+ people), the shape of the result (appearances by show plus attributed ideas), and the hard auth requirement via _apiKey. It does not mention pagination or result limits, keeping it just below top marks.

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?

Front-loads the core purpose in the first clause, then adds the disambiguating framing and a concrete example. Three sentences with no filler, though the quoted 'does this person...' formulation is slightly verbose relative to the information it adds.

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?

There is no output schema, so the description must orient the agent on results, and it does describe the return content (appearances by show, ideas attributed). Auth and slug provenance are covered. Minor gaps remain around result size/ordering, but nothing essential for a correct call is missing.

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%, so both slug and _apiKey are already fully documented in the schema, including where the slug comes from and the key format. The description only repeats the slug through an example call and adds no new parameter meaning, which matches the baseline 3 when the schema does the heavy lifting.

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?

States a specific verb+resource (retrieve what one person has said across podcasts, their appearances by show and attributed ideas) and explicitly carves out its scope against a near-neighbor by contrasting 'does this person keep making this claim, and where' with 'who is this person'. An agent can distinguish it from canon_search, canon_episode_get and canon_podcast_get without opening any schema.

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?

Gives clear usage context: it is for tracking whether a person repeats a claim and where, not for identity profiling, and it points to canon_search / canon_episode_get as the source of the slug. It stops short of naming the sibling to use instead (e.g. entity_profile) or stating explicit exclusions, so it is clear context rather than full when/when-not routing.

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