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

Annotations already declare readOnlyHint=true, destructiveHint=false, etc. The description adds substantial behavioral context: it states the tool pulls latest 10-K financials from SEC EDGAR/XBRL with correct handling of off-calendar fiscal years, and for drugs pulls FAERS adverse-event counts, FDA approvals, and trial counts. It also explains results sorting by primary metric and return of citation URIs. No contradiction 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?

The description is well-structured, beginning with common trigger phrases, then providing usage guidance, then type-specific details, and finally output format. Every sentence adds value without redundancy. It is front-loaded with the core functionality.

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's complexity (two entity types, multiple data sources, sorting, citations) and no output schema, the description covers key aspects: purpose, when to use, data sources, sorting behavior, and return format (paired data + citations). It mentions edge cases like off-calendar fiscal years. A slight gap is the exact structure of 'paired data', but overall it is sufficiently complete.

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% with both parameters described. The description enhances understanding by explaining what data each type retrieves (company yields financials, drug yields adverse events, etc.) and gives examples. This adds meaning beyond the schema's basic 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 performs side-by-side comparison of 2-5 companies or drugs. It uses specific trigger phrases like 'compare', 'rank', 'head to head' and distinguishes from siblings by emphasizing 'ALWAYS PREFER over sequential single-pack lookups' and 'Replaces 8–15 sequential lookups', which differentiates it from tools like entity_profile or ask_pipeworx.

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 when-to-use guidance: when user asks for 'X vs Y', 'which is bigger', etc. It also instructs to prefer this over sequential single-pack lookups. However, it lacks explicit when-not-to-use scenarios (e.g., for a single entity or historical trends), though these are implied. Overall, clear and helpful.

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