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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds significant behavioral context: data sources (SEC EDGAR for companies, FAERS for drugs), fiscal year handling, result sorting, and citation URI inclusion. This enriches the agent's understanding of side effects and output characteristics 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 a single paragraph but front-loaded with example queries, which aids quick comprehension. Every sentence adds value: usage guidance, data sources, sorting behavior, and efficiency claim. While it could be slightly more structured (e.g., bullet points), it remains concise given the amount of useful detail.

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?

Despite lacking an output schema, the description explains what the output contains (paired data + pipeworx:// citation URIs, sorted by primary metric) and how data is sourced and handled (fiscal year corrections). For a tool with only two non-nested parameters, this provides complete context for an agent to understand both input and output behavior.

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 descriptions for both parameters. The description adds semantic value by explaining what each type retrieves (e.g., 'type="company" pulls LATEST 10-K revenue + net income + cash + long-term debt') and gives concrete examples for values (tickers/CIKs for company, drug names). This goes beyond the schema's basic type and range constraints.

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 performs side-by-side comparison of 2-5 companies or drugs. It uses specific verbs like 'compare' and identifies the resource (companies/drugs). It distinguishes from siblings by recommending preference over sequential single-pack lookups, which contrasts with tools 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 provides explicit guidance to prefer this tool over sequential single-pack lookups when comparing entities. It lists example queries that trigger the tool. However, it does not explicitly state when not to use it, though the schema constraints (2-5 values) and examples imply single-entity cases are handled by other tools.

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