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

Annotations already mark readOnlyHint=true, openWorldHint=true, and idempotentHint=true. The description adds significant behavioral context: data sources (SEC EDGAR for company financials, FAERS for drug metrics), handling of off-calendar fiscal years, sorting by primary metric, and return format (paired data + citation URIs). It does not discuss error handling or rate limits, but the added detail exceeds what annotations provide.

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 a block of example queries that quickly communicate the tool's purpose. Every subsequent sentence adds value (sources, sorting, output format). It is relatively dense but not bloated; minor redundancy exists (e.g., 'side-by-side' and 'ONE parallel call' could be tighter). Overall well-structured and economical.

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 description covers entity types, data sources, sorting, and output format sufficiently for an AI agent to select and invoke the tool correctly. It does not mention pagination, time range for FAERS data, or potential limits (e.g., max 5 entities). However, given the tool's moderate complexity and the presence of annotations, the description is nearly complete—missing only minor operational 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 enums and constraints. The description adds rich semantics: for 'type=company' it specifies the exact data points pulled (revenue, net income, cash, long-term debt) and notes correct fiscal-year handling; for 'type=drug' it lists adverse-event counts, FDA approvals, and active trials. Examples like 'AAPL', 'MSFT' and 'ozempic', 'mounjaro' clarify valid values. This fully compensates for any schema minimalism.

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 opens with concrete example queries ('compare X and Y', 'X vs Y') and clearly states the core function: 'side-by-side comparison of 2-5 companies or drugs in ONE parallel call'. It distinguishes from sequential lookups, making the purpose unmistakable and differentiated from siblings.

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 advises 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', providing clear when-to-use guidance. It also gives example patterns ('which is bigger', 'rank these companies') and contrasts with sequential lookups, leaving no ambiguity about the tool's intended use case.

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