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

Annotations provide readOnlyHint=true and openWorldHint=false, so the description doesn't need to restate safety. It adds value by specifying the data scope (since 2021, age held constant, year-on-year changes) and the analytical purpose, which helps the agent set expectations for the response.

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 concise (two sentences), front-loads the key data points, and includes practical use cases. Every sentence adds value without redundancy.

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?

Given the annotations (read-only) and rich output schema (likely containing detailed premium history), the description covers the core purpose and usage. It could mention that results are read-only, but annotations handle that, so it's complete enough for an agent to call it correctly.

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 75%, with cert, gender, and smoker having descriptions. The description adds context for 'age' (but not explicitly) and explains the analytical output. It doesn't detail parameter interactions, but given the high schema coverage, it provides sufficient supplemental meaning.

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 shows a plan's premium history since 2021, including schedule filings, age-constant premium, year-on-year changes, and average. It distinguishes itself from related tools like 'insurer_increases' by focusing on a specific plan's premium movement, not insurer-wide increases.

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 explicitly states when to use it: for questions like 'has this gone up' and 'is this insurer stable'. It implies it is for premium trend analysis, differentiating it from sibling tools like 'estimate_renewal' (future projections) and 'insurer_increases' (insurer-level trends).

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