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

Annotations declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is already clear. The description adds substantial behavioral context: it explains the data source (SEC EDGAR/XBRL), handles off-calendar fiscal years (AAPL Sep, NVDA Jan), states that results are sorted by the primary metric, and notes the return format (paired data + pipeworx:// citation URIs). This goes well beyond what annotations provide.

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 information-rich, with no filler sentences. Every clause adds value—trigger phrases, data sources, edge-case handling, sorting behavior, output format, and efficiency claim. It is slightly longer than the absolute minimum, but for a tool with two distinct modes (company vs drug) and such specific behaviors, the length is justified. The use of ALL CAPS for the key directive is a purposeful structural choice.

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?

There is no output schema, so the description must cover the return value—and it does. It promises 'paired data + pipeworx:// citation URIs per entity' and details the metrics included per entity type, plus the sorting behavior. It also covers constraints (2–5 entities, one parallel call) and edge cases (off-calendar fiscal years). For a comparison tool of this complexity, this is complete enough for an agent to invoke it correctly and interpret results.

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?

Although schema coverage is 100% (with a type enum and values array descriptions), the description enriches the parameter semantics significantly. It explains what each type actually retrieves (e.g., company → revenue, net income, cash, long-term debt; drug → FAERS adverse-event counts, FDA approvals, trial counts). It also clarifies that values must be 2–5 entities and that results are sorted by the primary metric, which helps the agent construct valid inputs and interpret outputs.

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 begins with a clear verb-resource pairing: 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It includes multiple trigger phrases ('Compare X and Y', 'X vs Y', 'rank these companies'), making the tool's intent unmistakable. It also distinguishes itself from sequential single-tool lookups, a key differentiator from sibling tools like entity_profile.

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 tool explicitly states when to use: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' This is a strong directive with an alternative (sequential lookups), and the description goes further to specify which entity types map to which data sources (company → 10-K metrics, drug → FAERS/FDA/trial counts). This leaves no ambiguity about the intended use case.

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