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

Annotations already indicate read-only, idempotent, open-world, non-destructive. The description adds substantial behavioral context: data sources (SEC EDGAR/XBRL, FAERS, FDA), handling of off-calendar fiscal years, sorting by primary metric, and that it returns paired data plus citation URIs. This goes far beyond the annotations, providing rich behavioral detail without contradiction.

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 compact yet information-dense, starting with trigger phrases, then core behavior, then type-specific details and output. Every sentence adds value: scope, metrics, data sources, sorting, citations, and comparative benefit. No fluff or repetition, ideal for AI consumption.

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?

With no output schema, the description explains return values ('paired data + pipeworx:// citation URIs per entity'), covers both types' behavior, data sources, sorting logic, and even highlights edge-case handling (off-calendar fiscal years). It is complete for a tool of this complexity.

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%, but description adds significant meaning: type='company' explains what metrics are pulled (latest 10-K revenue, net income, cash, long-term debt), while type='drug' explains FAERS and FDA counts. Values format is clarified with examples and constraints (tickers/CIKs vs names, 2–5 items). This substantially enriches parameter understanding.

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 uses a specific verb ('compare') plus resource ('2–5 companies or drugs') and clearly differentiates from siblings by declaring 'ALWAYS PREFER over sequential single-pack lookups' and stating it does side-by-side comparison in one parallel call. It also lists concrete metrics per type, making the purpose unmistakable.

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?

Explicit trigger phrases are provided ('Compare X and Y', 'X vs Y', 'which is bigger', 'rank these companies'), and the description explicitly says to prefer this tool over sequential single-pack lookups. It distinguishes when to use company vs drug type and notes it replaces 8–15 sequential lookups, giving clear usage context.

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