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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds valuable behavioral context: it specifies data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), explains how off-calendar fiscal years are handled, and notes that output includes citation URIs. This goes beyond annotations, though it does not mention any rate limits or caching 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 moderately concise but front-loaded with common query patterns and a strong usage guideline. Every sentence contributes purpose or context. Minor redundancy (e.g., 'side-by-side comparison' is stated twice) but overall efficient for the information conveyed.

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 has only 2 parameters with 100% schema coverage, good annotations, and no output schema, the description is fully adequate. It covers purpose, usage, parameter details, and behavioral traits, leaving no significant gaps for an agent to misuse the 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?

Schema coverage is 100% (both parameters described). The description adds meaningful context: for the 'type' enum, it explains what data each entity type pulls (latest 10-K data for companies; adverse-event counts, FDA approvals, trial counts for drugs). For 'values', it provides concrete examples and constraints (tickers/CIKs for companies, drug names). This adds 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?

The description clearly states the tool's purpose: side-by-side comparison of 2-5 companies or drugs in one call. It starts with typical user query patterns (e.g., 'compare X and Y', 'X vs Y') and immediately distinguishes itself from sequential single-entity lookups, which are sibling tools like '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?

The description provides explicit guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' This tells the agent when to use this tool instead of alternatives. It also clarifies that results are sorted by primary metric for queries like 'largest' or 'most'.

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