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

Despite comprehensive annotations, the description adds critical behavioral details: it explains that for companies it pulls latest 10-K financials from SEC EDGAR/XBRL with correct off-calendar fiscal year handling (citing AAPL Sep, NVDA Jan), and for drugs it pulls FAERS adverse-event counts, FDA approval counts, and active trial counts. It also notes results are sorted by primary metric and that citation URIs are 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 a single dense paragraph but every sentence serves a purpose (examples, usage preference, data sources, sorting, output format). It is slightly lengthy but front-loaded with examples, making it effective. Minor improvement would be breaking into bullet points.

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 (two entity types with different data sources, constraints on count), the description covers all necessary aspects: what it compares, how many, data sources per type, sorting behavior, output format, and citation handling. No output schema exists, but the description adequately explains return values.

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%, but the description enhances understanding by specifying that values should be tickers/CIKs for companies and drug names for drugs, and it explains the return payload context (e.g., revenue, net income for companies; adverse events for drugs). This goes beyond the schema's enum 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 opens with concrete query examples like 'Compare X and Y' and 'X vs Y', immediately establishing the tool's purpose of side-by-side comparison of 2–5 companies or drugs in a single parallel call. It clearly contrasts with sequential single-pack lookups, making the function unmistakable.

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?

Explicit guidance to 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and the claim that it 'replaces 8–15 sequential lookups' provides strong usage direction. The description gives clear when-to-use and avoid scenarios, fully addressing the decision heuristic.

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