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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive nature. The description adds significant behavioral detail: for companies, it pulls latest 10-K data from SEC EDGAR/XBRL with correct fiscal year handling; for drugs, it pulls FAERS, FDA approval counts, and trial counts. It also notes sorting by primary metric and returns paired data with citation URIs, going beyond 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 a single dense paragraph but front-loaded with trigger phrases and key information. Every sentence adds value, though breaking into sub-sections could improve scanability. It is concise and efficient.

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 (comparing up to 5 entities, two types, multiple metrics), the description covers what it does, when to use, specific data sources, sorting behavior, and return format (paired data + citation URIs). No output schema exists, but the description sufficiently describes the output scope, making it complete for correct invocation.

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?

Input schema has 2 parameters with 100% coverage, but the description adds semantic value: for 'values', it gives examples of tickers/CIKs and drug names, and specifies min/max items. This provides context that the schema alone does not, such as the format of values for each entity type.

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 with specific metrics per type. It includes trigger phrases like 'X vs Y' and 'compare', and distinguishes itself from sibling tools like entity_profile by explicitly noting it replaces multiple sequential lookups.

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 says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', providing clear when-to-use guidance. It also implies not to use for single entity lookups, and explains it replaces 8–15 sequential lookups, reinforcing the use case.

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