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

The description adds substantial behavioral context beyond the annotations: it discloses data sources (SEC EDGAR/XBRL, FAERS, FDA), handles off-calendar fiscal years, sorts results by primary metric, and returns paired data with pipeworx:// citation URIs. It also notes that it replaces 8–15 sequential lookups. This is rich, non-contradictory information that goes well beyond the readOnly/idempotent hints.

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 every sentence earns its place: trigger phrases, explicit preference guidance, data source details for both types, sorting behavior, and output format. It is front-loaded with the most important usage examples and remains well-structured despite its length, which is appropriate given 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?

The description is fully complete for a read-only comparison tool with good annotations and a simple schema. It explains purpose, usage, data sources, edge-case handling (off-calendar fiscal years), sorting, and output structure (paired data + citation URIs). No output schema exists, so the description fully compensates.

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 covers both parameters with descriptions, so the baseline is 3. However, the tool description adds significant meaning beyond the schema by explaining what each type pulls (company: latest 10-K financials; drug: FAERS counts, FDA approvals, trial counts) and how the values are interpreted (e.g., tickers/CIKs for companies, drug names for drugs). This elevates the score above the baseline.

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 purpose with specific verbs and resources: it performs side-by-side comparisons of 2–5 companies or drugs in a single parallel call. It distinguishes itself from sibling tools by explicitly contrasting with sequential single-pack lookups and providing representative trigger phrases like 'compare X and Y' and 'rank these companies.'

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 gives explicit when-to-use guidance, stating 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It also provides concrete examples of query types and specifies the data sources and sorting behavior, making it clear when this tool is the right choice.

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