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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds rich behavioral context beyond these: it specifies data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), details exactly which metrics are pulled (revenue, net income, cash, long-term debt; adverse-event counts, approval counts, trial counts), handles off-calendar fiscal years (AAPL Sep, NVDA Jan), and notes results are sorted by primary metric. This fully discloses behavior 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?

Although the description is long, every sentence earns its place. It is front-loaded with trigger phrases and the core purpose, followed by structured details per type and output format. No fluff or redundancy; the length is justified by the richness of needed behavioral context. It is well-organized and easily scannable.

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 the tool's complexity (two entity types, multiple metrics, citation URIs, sorting) and the absence of an output schema, the description is remarkably complete. It covers inputs (how to specify values), behavior (data sources, metrics, fiscal handling), and outputs (paired data + pipeworx:// URIs, sorted by primary metric). No critical missing information for an agent to invoke it correctly.

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 input schema already provides descriptions for both parameters (type enum with 'company'/'drug', and values array with min/max items and examples). The description complements this by explaining what each type actually does (company pulls 10-K financials, drug pulls regulatory/clinical counts) and gives formatting examples (tickers/CIKs vs. names). While schema coverage is 100%, the description adds meaningful semantic detail that helps the agent select valid values correctly, hence above 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 opens with explicit trigger phrases and clearly states the tool performs 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It names the specific entities (companies/drugs) and the action (comparison), and distinguishes itself from sibling tools like lookup and entity_profile by emphasizing parallelism and breadth. This is a specific verb+resource definition with clear differentiation.

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 when-to-use guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It also gives concrete trigger examples and explains the tool replaces 8–15 sequential lookups. It implies alternatives (single-pack lookups) and sets a clear preference rule, making it easy for an agent to decide when to invoke this tool.

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