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

Beyond the readOnly and idempotent annotations, the description discloses important behavioral caveats: the share-left figure is an estimate on a survivor-biased tape, CHANNEL_CHANGE teams are excluded unless explicitly requested, and 'a team is a rule over dates, never a claim about intent.' This adds real behavioral context beyond what annotations provide.

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 dense but front-loaded with the core team definition, followed by output fields, kind rules, filters, and caveats. It earns its length given the conceptual complexity, though it is somewhat run-on and could benefit from clearer structural separation.

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 properly covers the main return concepts: member count, dates, spread, headcount, share left, succession, and kind. It also includes essential caveats about data bias and intent. Minor gaps remain around sort/pagination behavior, but the tool is otherwise sufficiently specified for correct invocation.

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 low at 42%, and the description partially compensates by summarizing the filter categories (from/to firm, kind, move type, state, minimum members, date) and explaining include_members. However, it does not add detail for parameters like sort, limit, until, or cursor, leaving some semantic burden unmet.

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 precisely what the tool does: it finds clusters of advisors who left the same firm for the same firm from the same branch state within a 14-day chain. It also enumerates the team kinds and filter dimensions, making the tool's scope unmistakable and distinguishing it from sibling move/search tools.

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 clear context for when to use the tool—when the user wants advisor team clusters and can filter by firm, kind, move type, state, member count, or date. It does not explicitly name alternatives or say when not to use it, but the intended use case is strongly implied.

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