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

Annotations already indicate read-only, idempotent, open-world, and non-destructive behavior. The description adds valuable context beyond that: data sources (SEC EDGAR/XBRL for company, FAERS/FDA/trials for drug), handling of off-calendar fiscal years (AAPL Sep, NVDA Jan), sorting by primary metric, and output format (paired data + citation URIs). No contradictions 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?

The description is front-loaded with trigger phrases and immediately states the core purpose. Every sentence adds information: scope, data sources, fiscal year handling, sorting, output, and efficiency gains. Despite being detailed, it remains tight and readable with no filler. It earns its length.

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's complexity (different entity types, data sources, comparison logic) and no output schema, the description covers all essential aspects: when to use it, what data is pulled per type, edge cases (off-calendar fiscal years), result organization (sorted by primary metric), and output format (paired data + citation URIs). It is complete for an agent to select and invoke the tool correctly.

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?

Although the schema has 100% coverage, the description enriches parameter meaning substantially. For the 'type' parameter, it explains what data each enum value retrieves (latest 10-K financials vs. adverse events/approval/trials). For 'values', it clarifies the expected format for companies (tickers/CIKs) and drugs (names), with examples. This goes well beyond the schema's basic descriptions.

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 function: 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It specifies the verb 'compare', the resource 'entities' (companies/drugs), and the scope (2–5 items). It also distinguishes itself from sibling tools by referencing 'sequential single-pack lookups' and stating it replaces 8–15 sequential lookups. The trigger phrases further clarify intent.

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 when to use this tool: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It provides clear trigger phrases ('compare X and Y', 'X vs Y', 'rank these companies') and implies when not to use it (single entity lookups, which are handled by sibling tools like entity_profile). This gives strong usage direction.

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