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

Despite comprehensive annotations (readOnly, openWorld, idempotent, non-destructive), the description adds substantive behavioral context: it specifies data sources (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs), explains handling of off-calendar fiscal years, states sorting by primary metric, and mentions citation URIs. This goes well beyond annotation hints and gives the agent a clear model of what the tool does under the hood.

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 but every sentence earns its place. It front-loads trigger phrases and key action, then systematically covers data sources, parameter specifics, sorting behavior, and return value. While long, there is no fluff or repetition; it is efficiently structured for an AI agent to parse and act on.

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, the description is remarkably complete. It covers purpose, usage, data source provenance, edge-case handling (fiscal calendars), output ordering, and result format (paired data + citation URIs). No output schema exists, but the description sufficiently describes the return value. There are no major missing pieces for an agent to correctly use this tool.

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 significantly enriches parameter meaning. It explains what 'company' pulls (latest 10-K metrics) and what 'drug' pulls (FAERS counts, approvals, trials), provides concrete examples for the values array, and describes how results are ordered. This is far more informative than the schema's bare 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 starts with specific trigger phrases and states the core function: side-by-side comparison of 2–5 companies or drugs in ONE parallel call. It clearly distinguishes itself from siblings by emphasizing it replaces sequential single-pack lookups, and it specifies the supported entity types (company vs drug).

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 when-to-use guidance with natural language trigger examples and an explicit preference statement: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It also differentiates by entity type, explaining what data each type pulls, so an agent knows exactly when to invoke this versus a single-entity tool.

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