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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=true, so safety is covered. The description adds genuinely useful behavior beyond that: the default payload is lean, sections are opt-in, `output_format='json'` is larger/noisier and meant for programmatic chaining, and it flags that only the current team reliably indicates where a player is today whereas a team with no end year may have been left.

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 core purpose and slug provenance are front-loaded in the first sentence, and the closing sentence routes analytics cleanly. The middle paragraph is a dense wall of text with run-on clauses (notably the 'sports' enumeration) and partially repeats the schema's `include` documentation, so it is informative but not maximally tight.

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 carries the burden of describing returns and does so section-by-section, plus documents the lean default and serialization choice. It omits error/invalid-slug behavior and any rate or permission notes, but an agent has enough to call this 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?

Schema description coverage is 100%, so the baseline is 3. The description goes further by enumerating what each `include` value yields and cross-referencing downstream consumers (episode/podcast slugs for follow-up calls, entity slugs for `particle_entity_get`, occupation/field codes accepted by `guest_occupation`/`guest_field` and `particle_expertise_get`), though much of this overlaps the schema's own enum 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 states a concrete verb and resource ('Return a person's profile: name, current role, and bio') and pins the keying input to the canonical slug from `particle_person_resolve`. It also distinguishes adjacent capabilities by routing analytics to `particle_podcast_get_guest` and entity lookups to `particle_entity_get`, so an agent can separate it from siblings without opening schemas.

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?

It gives clear routing: obtain the slug via `particle_person_resolve`, use `particle_podcast_get_guest` for guest analytics, and request only the `include` sections needed since the default response is lean. There are no explicit when-not-to-use conditions (e.g. when a resolve-only answer suffices), but the context for correct use is strong.

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