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

Although annotations already declare this as a safe read operation (readOnlyHint, idempotentHint), the description adds rich behavioral detail: it pulls specific financial data points from SEC EDGAR/XBRL and FAERS, handles off-calendar fiscal years correctly, sorts results by primary metric, and returns paired data with citation URIs. This goes far beyond the annotations and helps the agent anticipate output and edge cases.

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 common trigger phrases and gets to the core purpose immediately. Every sentence adds substantive information: limits, data sources, fiscal-year handling, sorting, output format, and efficiency. Despite being lengthy, there is no fluff; each clause is necessary for correct tool use.

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 there is no output schema, the description takes on the burden of explaining return values, which it does ('paired data + pipeworx:// citation URIs'). It covers edge cases (off-calendar fiscal years), sorting behavior, and the exact data pulled per entity type, making the tool fully self-contained and well contextualized.

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?

The schema already fully describes both parameters with 100% coverage, so the baseline is 3. The description adds extra meaning by explaining how each 'type' value affects the data source (10-K metrics for company, FAERS counts for drug) and by giving concrete examples ('AAPL', 'ozempic'), which clarifies the intended format and behavior beyond the bare schema.

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 states a specific verb ('compare') and a specific resource ('2–5 companies or drugs'), and clearly delineates what the tool does: a side-by-side comparison in one parallel call. It also distinguishes itself from siblings by explicitly contrasting with 'sequential single-pack lookups' and mentioning the ability to rank or compare, which is unique among the sibling tools.

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 trigger examples ('X vs Y', 'which is bigger') and a strong directive ('ALWAYS PREFER over sequential single-pack lookups'), telling the agent when to use this tool instead of alternatives. It also implies usage boundaries by limiting to 2–5 entities and clarifying data sources per type, giving clear context without needing to mention exclusions explicitly.

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