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

With no annotations, the description carries the full burden and does so well: it discloses that values are annualized YTD run rates rather than actual full-year revenue, that state filters by headquarters, that banks and CUs use non-identical FTE definitions, that invalid denominators and perimeter breaks are excluded from ranks but counted, and that cert/cu_number keeps the full peer rank. Remaining gaps are pagination and output/auth behavior.

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?

It is dense and front-loads the metric and ranking scope, but the body is a choppy sequence of clause fragments rather than organized sentences, making it harder to scan for a specific fact. Little is wasted, but the structure could be tightened into grouped points.

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 9-parameter, annotation-free tool with no output schema, the definition covers the metric, ranking, and caveats competently but omits pagination semantics, result shape, and half the parameters. An agent can call it, but not confidently for paging or quarter/min_assets filtering.

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% across 9 parameters, so the description must compensate. It usefully documents sector values ('both'/'bank'/'credit_union'), revenue_basis ('net' default vs 'gross'), the state=HQ semantics, and the cert/cu_number selection behavior, but leaves limit, offset, quarter, and min_assets entirely unexplained, so it only partially covers the surface.

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 the exact metric (revenue per employee/FTE) and the operation (same-quarter state ranking across banks and/or credit unions), which is specific enough to separate it from siblings like bank_metric_series or peer_compare. It stops short of explicitly naming an alternative tool or contrasting scope, so it is clear but not fully differentiating.

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?

Usage is implied rather than stated: the definitional detail tells an agent this is the tool for the per-employee efficiency metric, and 'No raw SQL' signals a canned query. There is no explicit when-to-use/when-not guidance and no sibling tool named as the alternative for adjacent needs.

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