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

Annotations (readOnlyHint=true, destructiveHint=false) already establish safety. The description adds substantial behavioral context beyond annotations: it names data sources (SEC EDGAR/XBRL, FAERS), explains off-calendar fiscal year handling, notes results are sorted by primary metric so 'largest' reads off the top, and discloses it returns paired data with pipeworx:// citation URIs. No contradictions.

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 trigger phrases and a clear directive, then tightly packs type-specific behavior, data sources, sorting, and return format. Every sentence carries meaningful information; despite being long, there is no filler or repetition.

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 2 parameters and no output schema, the description is exceptionally complete: it covers purpose, when to use, data sources, edge cases (off-calendar fiscal years), result ordering, return format (paired data + citation URIs), and performance benefit (replaces 8–15 calls). This gives an agent everything needed to invoke correctly.

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?

Input schema covers 100% of parameters with descriptions, so baseline is 3. The description enriches parameter semantics by explaining what each type value pulls (e.g., for company: latest 10-K revenue, net income, etc.; for drug: FAERS counts, FDA approvals) and how results are ordered. This goes beyond the schema's enums and array descriptions, though it doesn't add new format details beyond the 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 opens with concrete trigger phrases ('Compare X and Y', 'X vs Y') and states the tool performs 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It clearly distinguishes itself from sibling tools like entity_profile by emphasizing multi-entity comparison in a single call, and notes it replaces 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 instructs 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' providing clear when-to-use guidance. It also gives example query patterns and describes the two entity types (company/drug) and the data each pulls, making the usage context unmistakable.

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