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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context: data sources (SEC EDGAR for companies, FAERS for drugs), handling of off-calendar fiscal years, sorting by primary metric, and citation URIs. However, it does not mention rate limits or potential performance characteristics, which would be useful.

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 and front-loaded with common query patterns. Every sentence serves a purpose, with no wasted words. It efficiently conveys the tool's function, when to use it, and key behavioral details.

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 tool has only 2 parameters, no output schema, and annotations that cover side effects, the description provides complete context. It explains input format, output behavior, data sources, and even edge cases like off-calendar fiscal years. The agent has all necessary information to invoke the tool 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 100%, but the description adds significant meaning beyond the schema. It explains what each entity type returns (companies: financial metrics; drugs: adverse event counts, etc.), provides examples for the values parameter, and clarifies sorting behavior. This helps the agent correctly construct the input.

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 uses specific verbs like 'compare', 'side-by-side comparison', and 'head to head' to clearly state the tool's purpose. It explicitly distinguishes from siblings by stating 'ALWAYS PREFER over sequential single-pack lookups' and gives example queries that cover the intended use 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?

The description provides explicit guidance on when to use the tool (comparing 2-5 companies or drugs, head-to-head) and when not to (avoid sequential lookups). It also specifies the entity types and gives concrete examples, making it clear when this tool is the best choice.

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