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

Annotations declare read-only, idempotent, open-world, non-destructive hints. The description goes beyond by specifying data sources (SEC EDGAR/XBRL for companies, FAERS/FDA/clinical trials for drugs), handling of off-calendar fiscal years, and that results are sorted by primary metric. It also mentions returning citation URIs per entity.

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 a single dense paragraph of ~120 words, front-loaded with trigger phrases and core purpose. Every sentence adds unique value: triggers, purpose, type details, sorting behavior, efficiency claim. 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?

The tool has only two parameters and no output schema. The description covers input format, data sources, sorting, and return value nature (paired data + URIs). It also addresses edge cases like off-calendar fiscal years. This is sufficient for correct invocation and understanding results.

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% with basic descriptions. The tool description adds concrete formatting examples for `values` (tickers/CIKs for company, drug names) and explains the functional difference between type choices (financials vs. adverse event counts). This enriches the schema but the schema already had solid descriptions, so the addition is valuable but not essential.

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 does side-by-side comparison of 2-5 companies or drugs in one parallel call. It provides natural language trigger examples ("Compare X and Y", "X vs Y", etc.) and explicitly distinguishes from sequential lookups by stating it replaces 8-15 of them. The purpose is precise and leaves no ambiguity.

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 guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It lists query patterns that trigger this tool and notes it replaces many sequential lookups, making the efficiency case clear. It also implicitly excludes single-entity queries by contrasting with `entity_profile` sibling.

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