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

Annotations already declare readOnlyHint and idempotentHint, but the description adds substantial behavioral context: it names data sources (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs), specific metrics pulled, handling of off-calendar fiscal years, sorting by primary metric, and the return of citation URIs. This goes far beyond the annotations without contradicting them.

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 every sentence carries useful information: trigger phrases, core function, preference over alternatives, per-type behaviors, sorting, and output format. It is front-loaded and organized, but the density makes it slightly less scannable than ideal.

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?

Despite lacking an output schema, the description clearly explains what the tool returns (paired data + citation URIs) and covers both entity types in depth. It handles complexity well—two modes, multiple metrics, fiscal-year nuances—making it complete for an agent to decide whether and how to invoke it.

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?

The schema already documents both parameters with 100% coverage, providing the baseline of 3. The description adds meaningful value by specifying exactly what data each 'type' retrieves (e.g., 10-K revenue/net income/cash/long-term debt vs. FAERS adverse-event counts), and giving examples for the 'values' array. This helps the agent choose valid inputs beyond the schema's basic enum and array constraints.

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 trigger phrases and states 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call,' which clearly identifies the verb (compare) and resource (entities). It also distinguishes itself from 'sequential single-pack lookups,' making it unmistakable what this tool does relative to 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 gives the explicit directive 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' and provides concrete query examples like 'X vs Y' and 'rank these companies.' This tells the agent exactly when to use this tool and what to avoid.

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