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

Even though annotations already declare read-only, idempotent, and non-destructive behavior, the description adds substantial context: it details specific data sources (SEC EDGAR/XBRL, FAERS), mentions handling of off-calendar fiscal years (AAPL, NVDA), describes result sorting, and notes citation URIs. This goes well beyond the structured annotations and fully discloses behavioral traits.

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 front-loaded with user-intent triggers and a clear core statement, followed by compact details about data sources, sorting, and returns. It is slightly repetitive in emphasizing the preference over sequential lookups twice, but every sentence carries useful information and the structure is logical.

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 no output schema, the description sufficiently communicates what the tool returns ('paired data + pipeworx:// citation URIs per entity') and how results are sorted. It covers the main behavioral nuances and data sourcing, though it could mention potential limits like missing data or response format edge cases; overall it is complete for practical use.

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%, so the baseline is 3, but the description enriches parameter meaning by explaining what each 'type' pulls (e.g., 10-K revenue/net income/cash/long-term debt for companies; FAERS counts for drugs) and gives explicit value examples. It adds value beyond the schema without fully documenting every potential edge case or format restriction.

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 comparisons of 2–5 companies or drugs in one parallel call, with specific trigger phrases like 'X vs Y' and 'which is bigger'. It distinguishes from sequential single-entity lookups by explicitly preferring this tool for comparisons, and it conveys the concrete resource (SEC EDGAR/XBRL and FAERS data) used.

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 example queries and states 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It also differentiates behavior by type (company vs. drug) and clarifies output sorting, giving the agent clear conditions for selecting this tool over alternatives.

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