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

Despite annotations already declaring readOnlyHint=true and destructiveHint=false, the description adds significant behavioral context: data sources (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs), handling of off-calendar fiscal years, sorting by primary metric, and return of paired data with citation URIs. This goes well beyond what annotations provide and fully discloses the tool's 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 concise yet information-dense. It front-loads trigger phrases and the core function, then provides essential usage guidance and type-specific details. Every sentence earns its place, and the efficiency claim ("Replaces 8–15 sequential lookups") adds practical value. No redundancy or fluff.

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?

For a tool with two entity types and no output schema, the description is remarkably complete. It explains the return format (paired data + citation URIs), sorting behavior, data sources, and limitations (2–5 entities). It also covers edge cases like off-calendar fiscal years. Given the complexity and lack of output schema, this is fully adequate for an agent to 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?

The schema already covers both parameters (type and values) with descriptions, so baseline is 3. The description adds meaning by explaining what each type pulls (revenue, net income, cash, debt for companies; adverse-event counts, approval counts, trial counts for drugs) and clarifying the values format (tickers/CIKs or drug names). This enriches the parameter semantics beyond schema alone.

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 parallel call. It lists explicit trigger phrases ("Compare X and Y", "X vs Y", "rank these companies") and distinguishes itself from sequential single-pack lookups, making the purpose unambiguous.

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?

It explicitly says "ALWAYS PREFER over sequential single-pack lookups when comparing entities", providing a strong when-to-use guideline. It also gives when-not-to-use by implying not to use sequential lookups, and it describes type-specific use cases (company vs drug). While it doesn't name a specific sibling tool, the guidance about avoiding sequential lookups is clear and actionable.

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