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

Annotations already declare read-only, idempotent, non-destructive, closed-world behavior. The description adds return-content context not in the annotations: log vs linear bucketing and a count of parts that state the value at all, which helps the agent interpret output despite the missing output schema.

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?

Two sentences, front-loaded with the core purpose, with the usage contrast appended. Slightly dense with em-dash clauses but every clause carries information; nothing is padded.

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?

A five-parameter tool with no output schema and zero schema coverage needs more: the shape of filters, valid field naming, and bucket default behavior are left unexplained. It does describe the return semantics (bucket counts plus coverage count), which partially offsets the missing output schema.

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 0%, so the description must compensate. It references all five params conceptually (family, field, log/linear, buckets, filtered slice), but gives no format details for filters, no constraint that field must be numeric, and no note on defaults.

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 operation: how a numeric spec is distributed across a filtered slice of a family, with bucket counts and a coverage count. It clearly delineates against list-style output ('what a top-N list cannot'), though it never names the sibling (search_parts/recommend_parts) that provides top-N lists.

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 a clear usage frame: answers 'what is available' questions rather than ranked retrieval. This contrasts with top-N tools implicitly, but there are no explicit exclusions or named alternatives, so the agent must infer which sibling to pick.

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