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

Annotations already declare readOnly/idempotent/non-destructive, but the description adds substantial behavioral context: it identifies the exact data pulled for companies (10-K revenue, net income, cash, long-term debt from SEC EDGAR/XBRL) and drugs (FAERS adverse-event counts, FDA approval counts, active trial counts), notes correct handling of off-calendar fiscal years, explains sorting by primary metric, and mentions the return format with citation URIs.

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?

Though dense, every sentence contributes unique value: trigger phrases, command preference, data sources, fiscal-year handling, type-specific behavior, sorting, return format, and efficiency claim. The structure front-loads the core call to action and then expands with necessary detail without 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 two-parameter tool with no output schema, the description is exceptionally complete. It covers what data will be returned, how results are ordered, that paired data and citation URIs are included, and why this should be used instead of multiple lookups. The behavior for both company and drug types is fully disclosed.

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?

The schema already documents both parameters with 100% coverage, but the description adds meaning beyond the schema: it maps type='company' to specific financial data fields, type='drug' to specific regulatory/adverse-event counts, and provides concrete examples for values (['AAPL','MSFT'], ['ozempic','mounjaro']) with mention of tickers/CIKs. This enriches parameter understanding significantly.

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 query examples and explicitly states 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It clearly identifies the resource (companies/drugs) and the action (comparison), and distinguishes itself from sequential single-pack lookups by saying 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.'

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: whenever a user is comparing entities ('Compare X and Y', 'rank these companies'), and explicitly calls out the alternative approach it should replace ('ALWAYS PREFER over sequential single-pack lookups'). It also differentiates behavior for company vs drug types, making the appropriate use case very clear.

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