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

Beyond annotations (readOnlyHint=true, destructiveHint=false), the description discloses rich behavioral details: data sources (SEC EDGAR/XBRL for company, FAERS for drug), handling of off-calendar fiscal years (AAPL Sep, NVDA Jan), sorting by primary metric, and output format (paired data + citation URIs). This exceeds what annotations provide and accurately represents the tool's behavior.

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 long but every clause serves a purpose: trigger examples, parallel-call emphasis, type-specific data sources, fiscal year handling, output sorting, and efficiency gains. It is front-loaded with direct examples, and while dense, it remains scannable. A few minor redundancies exist (e.g., repeating 'parallel call'), but overall it is well-structured for an AI agent.

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 compensates by explaining the return format ('paired data + pipeworx:// citation URIs per entity') and sorting behavior. It also covers edge cases (off-calendar fiscal years) and both entity types, making the tool's behavior fully understandable. There are no missing critical pieces for a comparison 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% (type and values both documented), so baseline is 3. The description adds significant value by explaining the meaning of each type ('company' pulls latest 10-K metrics, 'drug' pulls FAERS counts) and clarifying the values format (tickers/CIKs for company, drug names for drug). This extra contextualization goes beyond the schema's simple enum and array description.

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 specific verbs and resources: 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It clearly distinguishes from siblings like entity_profile by emphasizing the comparative, multi-entity focus and the 'head to head' use cases. The explicit trigger phrases ('X vs Y', 'rank these companies') make the tool's 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?

The description provides explicit when-to-use guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and lists example queries that should trigger this tool. It also mentions it 'Replaces 8–15 sequential lookups,' reinforcing the context for use. It does not explicitly name alternatives, but the contrast with single-pack lookups is clear.

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