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

Annotations indicate read-only and idempotent, but the description adds substantial behavioral context: results are sorted by primary metric, off-calendar fiscal years are handled correctly, it runs in parallel, and it returns paired data with citation URIs. This goes well beyond the sparse annotation hints.

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 dense, front-loading usage triggers and purpose before diving into data details. Each clause earns its place: trigger phrases, entity-type behavior, metric notes, and the efficiency gain. Despite its length, it is highly structured and free of 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?

Given the tool's moderate complexity (two types, multiple metrics, grouping, sorting, citations) and the absence of an output schema, the description fully explains what the tool returns: paired data, sorted results, and citation URIs. It also covers edge cases like fiscal-year handling, making it sufficiently complete for an agent to invoke 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 already covers parameter names and types, but the description enriches semantics by specifying exactly what each type pulls: company type retrieves 10-K revenue/net income/cash/debt via SEC EDGAR/XBRL, while drug type retrieves FAERS adverse events, FDA approvals, and trial counts. It also clarifies value formats with examples, adding meaning that the schema alone does not provide.

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 performs side-by-side comparison of 2–5 companies or drugs in one parallel call, with explicit trigger phrases and examples. It distinguishes itself from siblings by emphasizing that it replaces 8–15 sequential lookups and is preferred for entity comparisons.

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 explicitly instructs when to use it ('ALWAYS PREFER over sequential single-pack lookups when comparing entities'), lists trigger phrases, and notes the alternative (sequential lookups). It also defines behavior per entity type, giving clear context for choosing this tool over others.

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