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

Annotations already declare the tool safe (readOnlyHint, idempotentHint, not destructive). The description adds valuable behavioral details: results sorted by primary metric, off-calendar fiscal year handling, citation URIs returned. 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 concise at ~120 words, front-loaded with actionable examples, and every sentence provides essential information without redundancy. It is well-structured, covering purpose, usage, parameters, and behavior efficiently.

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 simple parameter set (2 params, no output schema), the description fully covers what the tool returns (paired data + citation URIs), how data is sorted, and the data sources. No gaps remain for effective tool selection and 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?

The description adds significant meaning beyond the schema: for 'type=company' it specifies pulling latest 10-K revenue, net income, cash, long-term debt; for 'type=drug' it lists FAERS adverse events, FDA approval counts, active trial counts. This enriches the parameter documentation even though schema coverage is 100%.

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 specifies a very clear purpose: side-by-side comparison of 2-5 companies or drugs in one call. It uses explicit example queries (e.g., 'X vs Y', 'which is bigger') and distinguishes from siblings by stating it replaces 8-15 sequential lookups, contrasting with a single-entity tool 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 usage guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It covers both when to use (comparison queries, ranking) and what each entity type does (company financials, drug regulatory data), effectively contextualizing against siblings.

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