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

Annotations already state that the operation is read-only, idempotent, non-destructive, and closed-world. The description adds useful behavioral detail about output shape: practices are returned one line each, or as a menu by theme when there are many.

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 three tight sentences, front-loading purpose, then usage, then return behavior and follow-up. There is no wasted text.

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 listing tool with five optional parameters, full schema coverage, and no output schema, the description gives enough context about input choice, output format, and the next-step tool. Minor detail about how evidence strength is represented could be added, but nothing essential 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 coverage is 100%, so the schema carries the detailed parameter documentation. The description still adds value by clarifying that employees or a size band can be supplied and that theme, stage, and priority are optional refinements.

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 verb and resource: list practices for adopting AI, filtered by company size and rated by evidence strength. This clearly distinguishes it from sibling tools such as get_evidence, which fetches supporting studies and cases, and find_company_cases.

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 concrete usage examples ('Where should we start with AI?' / 'What should a 120-person manufacturer do first?') and routes the agent to get_evidence for deeper detail. It does not explicitly cover when not to use it versus every sibling, but the routing context is clear.

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