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

Annotations already indicate safe, read-only, idempotent behavior. The description adds substantial detail: for type='company' it pulls specific financial metrics from SEC EDGAR/XBRL with correct off-calendar fiscal year handling; for type='drug' it pulls FAERS counts, FDA approvals, and trial counts. It also describes sorting by primary metric and output format (paired data + 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 front-loaded with example queries and gets straight to the point. Every sentence earns its place—example phrases, usage guidance, detailed data sources for both types, sorting behavior, and output format. It is appropriately sized for the complexity and has no filler.

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?

No output schema is provided, but the description explains return values ('paired data + pipeworx:// citation URIs per entity') and sorting behavior. It covers both entity types thoroughly, mentions off-calendar fiscal year handling, and explains why this tool replaces many consecutive calls. The description is complete for an agent to understand when and how to invoke it.

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 significantly adds meaning: it explains the two enum values ('company' pulls SEC data with fiscal year handling, 'drug' pulls FAERS/FDA/trial data) and clarifies the values array format (tickers/CIKs for company, drug names, 2-5 items). This goes well beyond the schema's short 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 starts with clear example queries ('Compare X and Y', 'X vs Y') and explicitly states the tool does 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It distinguishes from siblings by noting it 'Replaces 8–15 sequential lookups' and is preferred over sequential single-pack lookups.

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 gives strong guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It explicitly tells when to use (comparing entities) and implies when not to use (single entity lookups). The alternative (sequential lookups) is mentioned as something to avoid.

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