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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds detailed behavior: for company type, it pulls 'LATEST 10-K revenue + net income + cash + long-term debt from SEC EDGAR/XBRL' and handles off-calendar fiscal years; for drug type, it pulls 'FAERS adverse-event counts, FDA approval counts, active trial counts.' It also notes results are sorted by primary metric and returns 'paired data + pipeworx:// citation URIs per entity.' No contradiction.

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 fairly long but every sentence adds value: natural language triggers, purpose, data sources, handling of special cases, sorting, and return format. It is front-loaded with common queries. Slight redundancy ('side-by-side comparison' repeated) but overall efficient.

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?

Given the complexity (two entity types, multiple data sources, sorting, citation URIs), the description covers purpose, parameters, behavior, and return format. There is no output schema, but the description explains what is returned: 'paired data + pipeworx:// citation URIs per entity.' It also mentions handling of off-calendar fiscal years, showing completeness.

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% and both parameters have descriptions and enums. The description adds context: explains type values ('company' or 'drug') with examples ('tickers/CIKs' for company, 'drug names' for drug), and clarifies that values must be 2-5 items. This enriches the schema but schema already does a solid job.

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 starts with natural language queries ('Compare X and Y', 'X vs Y') and clearly states it provides 'side-by-side comparison of 2-5 companies or drugs in ONE parallel call.' It explicitly distinguishes from siblings by preferring over sequential lookups. The verb 'compare' and resource 'entities' are precise.

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 usage scenarios: when user asks for comparison, ranking, or head-to-head. It advises to 'ALWAYS PREFER over sequential single-pack lookups,' implicitly excluding sequential lookups. It also states it replaces 8-15 sequential lookups, clearly defining its niche.

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