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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, so the agent knows it's a safe, repeatable read operation. The description adds valuable behavioral context: the baseline is computed from 3,100+ CEE engineering leaders, and it specifies that both counts must be for the same population. It also indicates the return contains percentages, delta, and a verdict, which complements the output schema. No contradictions with annotations.

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?

Every sentence in the description adds value: the core comparison is front-loaded, the population constraint is explained precisely, and the output is summarized. There is no filler or redundancy. The structure logically flows from main action to constraints to results.

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 tool with 3 parameters, full schema coverage, an output schema, and read-only annotations, the description is complete. It provides the baseline reference, the input constraints, and the nature of the return values. Nothing an agent needs to correctly invoke the tool is missing.

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 all parameters are documented. The description goes beyond the schema by clarifying that 'managers' and 'senior_ics' must both count the same population (senior people who could plausibly hold a management role), and explicitly says to leave out junior/mid ICs on both sides. This prevents common misinterpretation and adds meaning beyond the raw field descriptions.

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 clearly states the tool's purpose: it compares a company's manager-vs-senior-IC split against the ELC community's baseline, then returns percentages, delta, and a verdict. The verb 'Compares' and the specific resource (manager-vs-senior-IC split) make it unambiguous, and it implicitly distinguishes itself from unrelated sibling 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 provides clear context on what inputs to use (senior population only, excluding junior/mid ICs) and explains the apples-to-apples requirement. While it doesn't explicitly state when to use this tool versus alternatives, the sibling tools are in different domains (launch readiness, business case, tool discovery) so no explicit exclusion is needed. The guidance is sufficient for correct usage.

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