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

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description adds rich behavioral detail: data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), correct handling of off-calendar fiscal years, sorting by primary metric so 'largest' reads off the top, and return format (paired data + citation URIs). This gives the agent a comprehensive understanding of what the tool does and how it behaves, without contradicting annotations.

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 compact yet densely informative. It opens with natural language examples, states the core function and preference rule, then details type-specific behaviors and output. Every sentence contributes value, and the structure guides the reader from overview to specifics without redundancy.

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 tool's moderate complexity (two entity types, up to 5 items) and the absence of an output schema, the description adequately covers return values ('paired data + pipeworx:// citation URIs per entity'), sorting behavior, and data sources. It also notes fiscal year handling, which is non-obvious. No critical information for selection or invocation is missing.

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 provides 100% coverage with clear descriptions for both parameters. The description adds further semantic value by explaining what each type pulls (e.g., 'latest 10-K revenue + net income + cash + long-term debt' for companies) and providing concrete examples for values (e.g., ['AAPL','MSFT']). While not exhaustive, this enriches understanding 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 clearly states the tool's function: 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It provides concrete trigger phrases, identifies the entity types, and distinguishes it from sequential single-pack lookups. This goes beyond a vague statement and effectively differentiates the tool from siblings like entity_profile.

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 says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' giving a clear directive for when to use this tool. It also explains the two entity types and their respective data sources, which guides selection. However, it does not explicitly mention alternative tools (e.g., entity_profile) or state when not to use it, so it falls short of a perfect score.

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