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

While annotations already declare readOnly, openWorld, idempotent, and non-destructive, the description adds substantial behavioral context: data source (SEC EDGAR/XBRL for companies, FAERS for drugs), specific financial fields pulled, handling of off-calendar fiscal years, result sorting by primary metric, and return format (paired data + citation URIs). This goes well beyond the annotations and no contradiction exists.

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 carries important information. It starts with trigger phrases, then explains functionality, data sources, edge cases, and output. It is slightly longer than strictly necessary but remains well-structured and front-loaded with the most critical usage context. Could be trimmed slightly, but 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?

Without an output schema, the description fully explains return values (paired data + citation URIs) and behavior. It covers edge cases (off-calendar fiscal years), sorting, and data sources. Given 2 parameters and no output schema, this description is complete for an agent to correctly invoke and interpret results.

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 enriches parameter meaning. It explains what 'type=company' returns (latest 10-K revenue, net income, cash, long-term debt) and what 'type=drug' returns (FAERS counts, FDA approval counts, trial counts). It also gives concrete value examples (AAPL, MSFT; ozempic, mounjaro) and notes sorting behavior, adding value beyond the schema.

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?

Purpose is crystal clear: 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It specifies the resource (companies/drugs), the action (compare), and the scope (2–5 entities). It also distinguishes itself from sequential single-pack lookups, which is a primary alternative.

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 states 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It provides trigger phrases ('compare X and Y', 'X vs Y', 'which is bigger'), which tells the agent exactly when to invoke this tool. It also clarifies data sources for each type, giving clear usage context.

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