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

Annotations already establish read-only, idempotent behavior; the description adds substantial context: SEC EDGAR/XBRL data for companies, FAERS/FDA/trial counts for drugs, off-calendar fiscal year handling, sorting by primary metric, and citation URIs in return. No contradictions with 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 dense but front-loaded with trigger phrases and includes all critical information. Each sentence contributes either usage guidance, data scope, or output behavior, avoiding fluff. It is somewhat long but appropriately so for a tool with dual entity types and distinct data sources.

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 no output schema, the description specifies what is returned (paired data + pipeworx:// citation URIs), how results are sorted, and the data domains for both entity types. It also addresses edge cases like off-calendar fiscal years, making it complete for a tool of this complexity.

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%, so baseline is 3. The description adds operational meaning: type='company' vs 'drug' determines the data source and fields, and values accept tickers/CIKs or drug names. This goes beyond the schema's basic text descriptions by explaining the consequences of parameter choices.

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 defines a side-by-side comparison of 2–5 companies or drugs in one parallel call, using explicit trigger phrases like 'X vs Y' and 'rank these companies'. It distinguishes itself from sequential single-entity lookups and specifies the data pulled for each entity type, making the tool's purpose unambiguous.

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?

It explicitly states 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' offering direct selection guidance. Examples of user intents ('X vs Y', 'which is bigger') clarify when to apply. The description's strong preference directive and trigger examples provide clear usage context.

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