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

Annotations already indicate read-only, idempotent, and nondestructive behavior. The description adds crucial behavioral context: it pulls from SEC EDGAR/XBRL for companies, FAERS for drugs, handles off-calendar fiscal years, returns sorted results, and provides citation URIs. No contradictions.

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 a single paragraph but well-organized with trigger phrases upfront. It is dense but not overly verbose. Every sentence adds value, though it could be slightly more concise without losing clarity.

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 no output schema, the description explains return data (paired data + citation URIs) and behavior (sorted by primary metric). It covers both entity types, data sources, and edge cases (off-calendar fiscal years). Complete for the tool's 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?

Schema coverage is 100% with descriptions for both parameters. The description enriches semantics by explaining the format (tickers/CIKs for company, drug names), constraints (2–5 items), and how the primary metric is used for sorting. Adds value beyond 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?

The description clearly states the tool's purpose: side-by-side comparison of 2–5 companies or drugs. It includes natural language triggers ('compare X and Y', 'X vs Y') and explains the data sources for each type. It distinguishes itself from sequential single-entity lookups (e.g., entity_profile).

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?

Explicitly states when to use ('ALWAYS PREFER over sequential single-pack lookups when comparing entities') and provides clear context for each entity type (company vs drug). It explains the data pulled and sorting behavior, helping the agent decide when to invoke.

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