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

Annotations already declare the tool read-only, idempotent, and non-destructive, so the safety profile is clear. The description goes further by disclosing concrete behavioral traits: parallel execution, latest 10-K data pull, correct handling of off-calendar fiscal years, sorting by primary metric, and returned paired data with citation URIs. These add substantial value 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 dense and well-structured, front-loading trigger phrases and the core purpose. It is slightly verbose with the repeated trigger examples, but every sentence contributes to the agent's understanding, and the length is justified by the tool's dual-type scope and rich behavioral detail.

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 two-parameter tool with no output schema, the description is remarkably complete. It specifies data sources, return format (paired data + citation URIs), sorting behavior, scope (2–5 entities), and business context (replaces sequential lookups). The agent has everything needed 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?

Schema coverage is 100%, but the description enriches parameter meaning significantly. It maps type='company' to specific metrics (revenue, net income, cash, long-term debt) and type='drug' to FAERS counts, FDA approvals, and trial counts. It also clarifies value formats with examples and notes fiscal year nuances, providing crucial context beyond the schema's generic field 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 explicitly states 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call' and provides numerous trigger phrases ('X vs Y', 'rank these companies'). It distinguishes itself from siblings by declaring 'ALWAYS PREFER over sequential single-pack lookups' and 'Replaces 8–15 sequential lookups', making its unique role 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?

The description gives explicit when-to-use guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It also provides natural-language triggers that signal the appropriate context and explains type-specific usage (company vs. drug), clarifying which data each type retrieves. This makes the tool's selection criteria very clear.

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