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

Beyond the annotations (readOnly, idempotent, non-destructive), the description discloses detailed behavioral traits: it pulls specific data sources (SEC EDGAR/XBRL, FAERS, FDA), handles off-calendar fiscal years correctly, sorts results by the primary metric so 'largest' reads off the top, and returns paired data with citation URIs. This goes far beyond what annotations provide and sets clear expectations.

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: trigger phrases, explicit preference, data source details, fiscal-year handling, sorting behavior, and output format. It is front-loaded with usage examples and avoids any fluff or repetition. The structure flows from use case to mechanics to output, making it highly scannable.

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 moderate complexity (2 params, no output schema), the description is remarkably complete: it explains what data is returned (paired data + citation URIs), how results are ordered, which data sources are used, and why it is efficient. There are no obvious gaps; an agent would know exactly what happens when invoking this tool.

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 the schema already describes both parameters, the description adds substantial meaning: it explains that type='company' retrieves 10-K revenue/net income/cash/debt, type='drug' retrieves FAERS/FDA counts, and gives concrete value examples like ["AAPL","MSFT"]. This enriches the schema definitions by clarifying what data each entity type yields and the expected input format.

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 uses the specific verb 'compare' with defined entity types ('companies or drugs'), cites exact trigger phrases, and explicitly frames it as a side-by-side comparison in one parallel call. It distinguishes itself from sibling tools by stating it is 'ALWAYS PREFER over sequential single-pack lookups' and notes it replaces 8–15 sequential lookups, making its unique role clear.

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?

Provides explicit when-to-use guidance through natural-language examples ('X vs Y', 'rank these companies') and states a clear preference over sequential lookups when comparing entities. It also differentiates between 'company' and 'drug' modes, giving context for each, though it does not name a specific alternative tool by section. The instruction to prefer it is strong and actionable.

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