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

Beyond the readOnly/openWorld annotations, it discloses the scoring basis (shares are of the person's scored lines; too-short lines carry no characteristics), the low_sample flag threshold of 20, the default one-year window, and exactly what include=['examples'] adds, including episode slugs and a playback link.

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-loaded with the semantic meaning of the output, then mechanics, then routing to alternatives. Dense but mostly earns its space; the second paragraph packs several unrelated points (scoring caveat, alternative tool, resolve prerequisite) rather than sequencing them cleanly.

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 return-shape burden and does so: it enumerates the bucket dimensions, the overall vs per-interval breakdown, the low_sample marker, and the structure of the optional examples section. An agent can call this correctly without further documentation.

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 already 100%, so the baseline is 3; the description earns extra by explaining why to add include=['examples'] and how its output chains into particle_podcast_get_episode. It also restates the default window behavior, though interval/output_format semantics are largely left to the schema.

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 names a specific resource and scope: how a person speaks on podcasts, broken into sentiment, hedging, vagueness and temporal-orientation buckets, overall and per week/month. It is clearly distinguishable from siblings such as particle_podcast_get_episode_timeseries or particle_person_get, and it explicitly is not the transcript-search tool.

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 concrete trigger questions ('is Sam Altman more cautious than he used to be', 'how often does she make predictions'), names the alternative path (particle_podcast_search_transcripts with person_slug and speaker_statement_characteristics) for finding specific lines, and states the prerequisite of resolving the person first.

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