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

Annotations only declare readOnlyHint: true, so the description carries the burden of behavioral detail. It adds valuable context: the default response is lean, optional sections require explicit requests, podcast appearances include follow-up slugs, and `output_format` choices affect LLM readability versus programmatic use.

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 compact, front-loaded with the core purpose, and every sentence serves a function: scope, optional sections, default behavior, and sibling-tool routing. No wasted words.

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 read-only lookup with complete schema documentation, no output schema, and no nested objects, the description supplies all necessary calling context: what the profile contains, how to get optional sections, what follow-up data is included, and when to use a different tool.

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 100%, so the description need not re-document parameters. It adds some contextual reinforcement around `include` sections, but mostly repeats the schema descriptions rather than providing new parameter-level meaning.

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 specific verb and resource: 'Return a person's profile: name, current role, and bio, keyed by the canonical person slug.' It clearly distinguishes from related tools by naming `particle_person_resolve` as the slug source and `particle_podcast_get_guest` as the analytics-focused alternative.

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 explicit routing guidance: use `particle_podcast_get_guest` for podcast-guest analytics with the same slug, and implies `particle_person_resolve` as a prerequisite for obtaining the canonical slug. It also explains when to request optional `include` sections.

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