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

With annotations already declaring read-only and idempotent hints, the description goes far beyond by disclosing data sources (SEC EDGAR/XBRL, FAERS), handling off-calendar fiscal years, result sorting by primary metric, return format (paired data + pipeworx:// citation URIs), and the parallel single-call nature. This adds substantial behavioral context without contradicting 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 typical, but it is front-loaded with trigger phrases and a preference statement, and every sentence adds behavior or context. It could be slightly condensed without losing core meaning, but it remains well-structured and efficient for the richness it conveys.

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, 2–5 items, data sources, sorting, output), the description covers type-specific behavior, input constraints, output format, sorting rationale, and the advantage over sequential lookups. With no output schema, this description makes the tool self-sufficient for an agent to select and invoke 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?

Although schema coverage is 100%, the description enriches parameter semantics by specifying exactly what each type pulls (company: latest 10-K revenue, net income, cash, long-term debt; drug: FAERS adverse-event counts, FDA approval counts, active trial counts) and clarifying value formats (tickers/CIKs vs names). This goes well beyond the schema's generic 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 defines a parallel side-by-side comparison tool for 2–5 companies or drugs, with explicit trigger phrases ('Compare X and Y', 'X vs Y', 'which is bigger') and a specific verb+resource: 'side-by-side comparison of 2–5 companies or drugs.' It distinguishes itself from sequential single-pack lookups and sibling tools like entity_profile, making the purpose unambiguous.

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 explicitly states when to use the tool: whenever the user requests a comparison, and even instructs 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It provides concrete example queries, type-specific behavior, and notes it replaces 8–15 sequential lookups, giving strong contextual and exclusionary guidance.

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