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

The description adds rich behavioral context beyond the safe-read annotations: it discloses data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), handling of off-calendar fiscal years, sorting by primary metric, output format (paired data + citation URIs), and performance benefit (replaces 8–15 sequential lookups). 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 longer than average but every sentence is informative: trigger phrases, use-case priority, data sources, sorting, output, and performance impact. It is front-loaded with usage examples and structured logically. A slight trim could help, but the complexity justifies the length.

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?

The tool is complex (two entity types, multiple data sources, no output schema) and the description covers all key aspects: what data is pulled, how results are sorted, what output to expect (paired data + URIs), and when to prefer it. It gives enough context for an agent to select and invoke it correctly without needing additional details.

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 descriptions for both parameters, providing a baseline of 3. The description adds extra meaning: it explains what each type pulls (financial metrics for 'company', adverse-event counts for 'drug') and gives examples for values (tickers/CIKs, drug names). This exceeds the schema's basic enum and array descriptions, enhancing parameter understanding.

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 function: side-by-side comparison of 2–5 companies or drugs in a single call. It provides explicit trigger phrases like 'Compare X and Y' and 'rank these companies', and distinguishes itself from sequential single-pack lookups, likely sibling tools like entity_profile.

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 this tool: 'ALWAYS PREFER over sequential single-pack lookups' and provides concrete query examples. It implies when-not-to-use (when not comparing entities) but does not name specific alternative tools, though 'sequential single-pack lookups' is a clear contrast.

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