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

Annotations already mark the tool as read-only, open-world, idempotent, and non-destructive. The description adds significant behavioral context: it makes a parallel call, sorts results by primary metric, handles off-calendar fiscal years correctly, returns paired data with citation URIs, and pulls from specific data sources (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs). This far exceeds the annotation coverage.

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 well-structured and front-loaded with trigger phrases. It efficiently conveys purpose, usage preference, data sources, sorting behavior, and return format. While somewhat lengthy, every sentence adds necessary context without fluff, justifying the length for the tool's complexity.

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 that there is no output schema, the description adequately explains return values ('paired data + pipeworx:// citation URIs per entity'). It covers entity types, data sources, sorting logic, fiscal year handling, and the comparison count range. This is a complete description for a moderately complex tool, especially with strong annotations.

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 provides descriptions for both parameters with 100% coverage, so the baseline is 3. However, the description adds semantic value by explaining what each 'type' pulls (10-K data vs. adverse-event/trial counts) and provides concrete examples for 'values' (tickers/CIKs vs. drug names), which helps agents construct valid parameter values.

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 comparisons of 2-5 companies or drugs in one parallel call, with specific trigger phrases. It distinguishes itself from sibling tools by explicitly stating 'ALWAYS PREFER over sequential single-pack lookups' and mentions it replaces 8-15 sequential lookups, making its unique value clear.

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 usage guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It gives concrete examples of queries that should trigger this tool and explains the different data sources for company vs. drug types. This clearly communicates when to use it over alternatives.

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