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

Annotations already indicate readOnlyHint and idempotentHint. The description adds significant behavioral detail: pulls LATEST 10-K data, correctly handles off-calendar fiscal years, returns pipeworx:// citation URIs per entity. No contradiction with annotations.

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?

Every sentence adds value, but the description is slightly verbose. However, it effectively front-loads with trigger phrases and key action, making it scannable.

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 complexity of comparing two entity types with different data sources, the description covers all necessary aspects: data freshness, sorting, citation URIs, and the efficiency gain over sequential lookups. No output schema exists, but return structure is described.

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% and descriptions are provided for both parameters. The description adds context about what each type pulls (financial metrics for 'company', adverse-event counts for 'drug') and clarifies that values can be tickers/CIKs or drug names.

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 starts with explicit natural language triggers and clearly states the tool performs side-by-side comparison of 2-5 companies or drugs. It differentiates from siblings like entity_profile by specifying parallel lookup and specific data sources (SEC EDGAR for companies, FAERS for drugs).

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 instructs to prefer this tool over sequential single-pack lookups when comparing entities. Provides clear examples of user queries that trigger this tool and explains sorting behavior ('largest/most reads off the top').

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