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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds rich behavioral context beyond annotations: it pulls from SEC EDGAR/XBRL for companies, FAERS/FDA counts for drugs, handles off-calendar fiscal years, sorts results by primary metric, and returns pipeworx:// citation URIs.

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: trigger phrases, when-to-prefer, per-type data sources, fiscal-year handling, sorting, output format, and efficiency. There is no filler or redundancy; it's front-loaded with the most actionable information.

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?

Even without an output schema, the description tells the agent what to expect: paired data, pipeworx:// citation URIs, and sorted results. It also covers entity type limits, data source specifics, and the parallel-call advantage, making the tool fully usable without additional context.

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?

While schema coverage is 100%, the description substantially enriches parameter meaning. It explains type-dependent behavior ('type="company" pulls LATEST 10-K revenue...' vs 'type="drug" pulls FAERS...'), provides value format examples, and clarifies the 2–5 entity constraint. This goes well beyond the terse schema 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 opens with explicit natural-language triggers like "Compare X and Y" / "X vs Y" and clearly states "side-by-side comparison of 2–5 companies or drugs in ONE parallel call." This gives a specific verb+resource and distinguishes it from single-entity lookups 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 description provides clear when-to-use guidance: "ALWAYS PREFER over sequential single-pack lookups when comparing entities." It also gives concrete examples of matching user queries. It does not explicitly name a sibling tool or enumerate when-not-to-use, but the preference rule is unambiguous.

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