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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint; description adds substantial behavioral context: data sources (SEC EDGAR/XBRL, FAERS, FDA), off-calendar fiscal year handling, sorting by primary metric, and pipeworx:// citation URIs per entity. This goes far beyond annotation metadata.

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 dense but every sentence serves a purpose: query examples, scope constraints, data source details, output format, and performance efficiency. It is front-loaded with the most actionable usage cues and contains no filler or redundant restatements.

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, multi-source data, sorting, citations) and lack of an output schema, the description is remarkably complete. It covers triggers, scope, data sources, edge cases (fiscal year handling), result ordering, output URIs, and performance benefits—fully standing in for return-value documentation.

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 enriches parameter meaning by explaining what each type pulls (company financial metrics vs drug adverse-event/trial data), providing concrete examples for values (tickers/CIKs, drug names), and noting the sorting behavior tied to the primary metric. This adds significant value beyond the schema.

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 explicitly states the tool's function with specific verbs ('compare', 'side-by-side comparison'), identifies the resource scope (2–5 companies or drugs), and distinguishes itself from 'sequential single-pack lookups'. This clearly separates it from 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?

It provides explicit trigger phrases ('X vs Y', 'rank these companies'), states 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', and notes the efficiency gain ('replaces 8–15 sequential lookups'), giving clear when-to-use guidance and an alternative approach to avoid.

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