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

Annotations already declare the tool read-only, open-world, idempotent, and non-destructive, so the description adds rich behavioral context beyond those hints. It details data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), handles off-calendar fiscal years correctly (AAPL Sep, NVDA Jan), explains result sorting by primary metric, and mentions paired data with citation URIs. No contradictions 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?

Although the description is longer than typical, every sentence earns its place: trigger examples, preference over alternatives, data source details, sorting behavior, and return format are all packed densely. It is front-loaded with user intents and ends with functional benefits. No filler or repetition with the schema.

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?

For a tool with only two parameters and no output schema, the description is remarkably complete. It covers both entity types, input formats, data provenance, fiscal-year nuances, sorting, and output structure (paired data + citation URIs). This gives an agent sufficient context to invoke the tool correctly and set expectations for results, even without an output schema.

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%, so the baseline is 3. The description adds value by providing concrete examples ('AAPL','MSFT' vs 'ozempic','mounjaro') and explaining type-specific requirements (tickers/CIKs vs drug names) beyond the schema's concise descriptions. It also clarifies behavior tied to parameters (off-calendar fiscal year handling) but does not introduce entirely new semantics, hence a 4 rather than 5.

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 performs side-by-side comparisons of 2–5 companies or drugs in a single parallel call. It specifies the verb "compare" and the resource (entities), and distinguishes from sibling tools by emphasizing it should replace sequential single-pack lookups. Trigger phrases like 'X vs Y' and 'which is bigger' make the intent 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?

Usage guidance is explicit: it lists exact user phrasings that should trigger this tool ('Compare X and Y', 'X versus Y', etc.) and states 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It also clarifies when to use type='company' vs type='drug', providing clear decision criteria relative to alternative tools.

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