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

Annotations already mark this as read-only and non-destructive, but the description adds substantial behavioral context: results are ranked, the returned slug is stable and reusable across many downstream person_slug parameters, bulk queries resolve independently, and output_format changes the serialization with a warning that JSON is noisier for LLM reading. This goes well beyond what annotations could convey.

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 front-loaded with the core action, followed by return-value semantics, then bulk usage, then alternatives. Every sentence earns its place, and the enumeration of downstream endpoints is dense but purposeful. There is no redundant phrasing.

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 resolution tool with no output schema, the description covers the essential operations: what input looks like, what output to expect (ranked candidates with slug), how bulk queries behave, and which sibling tools to use instead. An agent has enough information to both select and invoke this tool correctly in most scenarios.

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 reinforces query semantics and exposes the canonical-slug benefit, but it largely restates what the input schema already documents for limit, query, and output_format. It adds no critical parameter meaning beyond 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 opens with a specific verb and resource: 'Resolve a person by free-text name' and states the concrete output ('ranked candidates with the canonical person slug'). It distinguishes this tool from sister tools by explicitly contrasting it with particle_entity_resolve and particle_company_resolve, leaving no ambiguity about its niche.

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 provides explicit when-to-use rules: use it for resolving persons, use particle_entity_resolve for organizations/places/mixed/unknown kinds, and use particle_company_resolve for companies with a known ticker or domain. It also gives bulk-query guidance, covering the main usage decisions an agent must make.

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