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

The description goes far beyond the annotations by revealing the data sources (SEC EDGAR/XBRL, FAERS), handling of off-calendar fiscal years, result sorting by primary metric, and the return format with citation URIs. This adds valuable behavioral context that the annotations do not cover, such as underlying data provenance and ordering behavior.

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 long but information-dense. It front-loads with user query examples and then systematically covers preference, type-specific details, sorting, and output format. While some redundancy exists (e.g., 'ONE parallel call' and 'Replaces 8–15 sequential lookups'), each sentence contributes to the tool's understanding, so it earns its place.

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 absence of an output schema, the description covers essential return information (paired data, citation URIs), data sources, and sorting. It lacks explicit error handling or edge-case behavior (e.g., invalid tickers), but overall it provides a comprehensive picture for a tool of this complexity.

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 documents both parameters, the description enriches them by specifying what metrics each 'type' returns (revenue, net income for company; adverse events for drug) and provides concrete examples for 'values' (tickers/CIKs, drug names). This adds meaning beyond the schema's generic descriptions, including details like off-calendar handling.

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 states the tool performs side-by-side comparisons of 2-5 companies or drugs, with concrete examples like 'X vs Y' and 'rank these companies.' It distinguishes itself from sequential lookups by emphasizing a single parallel call, and details type-specific data sources, 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?

The description explicitly says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' which provides a clear directive for when to use this tool. It also lists example queries to signal appropriate usage, though it does not explicitly name sibling tools or state when not to use it beyond the comparison context.

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