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

Annotations already indicate read-only, idempotent, and open-world. The description adds rich behavioral context: it fetches latest SEC data for companies with correct fiscal year handling, pulls FAERS and FDA data for drugs, sorts results by primary metric, and returns citation URIs. No contradiction with annotations.

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 well-structured, starting with common queries and then detailing behavior per type. Every sentence adds value, but it is slightly verbose (e.g., listing multiple example queries could be condensed). Overall, it earns its length without excess.

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 2 parameters and no output schema, the description thoroughly covers input usage, per-type behavior, output format (sorted results, citation URIs), and when to prefer it over other tools (replaces 8-15 sequential lookups). No gaps for an AI agent to misunderstand.

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 the description adds significant meaning: it explains that 'type=company' pulls revenue, net income, cash, debt from SEC, and 'type=drug' pulls adverse event counts, FDA approvals, trial counts. For 'values', it clarifies format per type (tickers/CIKs vs. drug names) and repeats min/max constraints. This far exceeds the schema's basic 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 clearly states the tool's purpose: side-by-side comparison of 2-5 companies or drugs. It uses explicit verbs like 'compare', 'rank', and specifies the resources ('companies', 'drugs'). It distinguishes from siblings like 'lookup' by emphasizing parallel retrieval over sequential lookups.

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 explicitly says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and lists trigger phrases like 'compare X and Y' and 'X vs Y'. It also clarifies when to use each type ('company' for financials, 'drug' for pharma data) and implies exclusivity (not for single entity queries).

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