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

Annotations already provide readOnly/openWorld/idempotent hints. The description adds significant behavioral context: off-calendar fiscal years handled correctly, results sorted by primary metric, and returns paired data with pipeworx:// citation URIs. It also specifies the exact data sources for each entity type, which is beyond what annotations alone convey.

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, using bolded examples and clear sections. Every sentence carries weight: usage triggers, data specifics, sorting behavior, and return format. Despite its length, it avoids redundancy and is easy to scan, making it appropriately sized 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?

For a tool with two parameters and no output schema, the description covers all essential aspects: what it does, which data is fetched for each type, how results are sorted, what citations are included, and the performance benefit. It sufficiently informs an agent about invocation and expected output without unnecessary elaboration.

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 type and values, but the description enriches both. It explains that type='company' pulls SEC EDGAR/XBRL data (revenue, net income, etc.) and type='drug' pulls FAERS/FDA/trial counts, which is far more useful than the schema's simple enum listing. The examples and max/min constraints for values also add practical meaning.

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 states a specific verb and resource: side-by-side comparison of 2-5 companies or drugs in one parallel call. It clearly distinguishes from sibling tools like entity_profile by emphasizing it's for comparing multiple entities and should be preferred over sequential lookups. The detailed examples of natural language queries further clarify the purpose.

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?

Explicitly states when to use: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and mentions it replaces 8-15 lookups. It also clarifies the types of things that can be compared (companies by financials, drugs by adverse events/trials), providing clear context for selection versus alternatives.

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