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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds significant behavioral context: it pulls latest 10-K data from SEC EDGAR/XBRL, handles off-calendar fiscal years correctly (e.g., AAPL Sep, NVDA Jan), sorts results by primary metric, and returns paired data with citation URIs. This far exceeds 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 front-loaded with the most important trigger queries. Every sentence adds distinct value—entity type behavior, data sources, sorting, output format, and performance comparison. It is appropriately sized for a complex tool, with no redundancy or filler.

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?

Despite having no output schema, the description explains what is returned ('paired data + pipeworx:// citation URIs per entity') and that results are sorted. It covers both entity types, their data sources, and the handling of fiscal years. For a tool of this complexity, the description is complete and self-contained.

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 description coverage is 100% (both parameters described). The description enriches the schema: for 'type', it explains exactly what data is pulled (company → 10-K metrics; drug → FAERS, FDA, trials). For 'values', it gives concrete examples (tickers, drug names) and the allowed range (2–5). This adds substantial meaning beyond the raw 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 begins with concrete example queries ('Compare X and Y', 'X vs Y', etc.) and explicitly states the tool does side-by-side comparison of 2–5 companies or drugs. It distinguishes itself from sequential single-pack lookups by saying 'ALWAYS PREFER over sequential single-pack lookups' and explains the specific data sources per entity type, making the purpose highly specific and clear.

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 when-to-use instruction: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It also provides example queries that trigger usage, and contrasts with alternative approaches (sequential lookups). No when-not is needed given the clear domain of comparison.

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