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

While annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, the description adds significant behavioral context: it pulls from SEC EDGAR/XBRL for companies and FAERS for drugs, correctly handles off-calendar fiscal years (AAPL, NVDA), sorts results by the primary metric, and returns paired data with citation URIs. These details go well beyond the annotations and inform the agent about data provenance and output ordering.

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 about eight sentences, but every clause serves a purpose: trigger phrases, core function, preference directive, type-specific data details, sorting behavior, output format, and performance benefit. It is front-loaded with the most important usage cues and avoids repetition. The structure is logical, starting with user intents and proceeding to behavior and output.

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 entity types and multiple behaviors, the description is remarkably complete. It covers when to use, what data each type returns, fiscal-year handling, sorting, output format (paired data + citation URIs), and even notes it replaces many sequential lookups. The schema and annotations handle parameter constraints and safety, so the description leaves no significant gap for an agent to select and invoke the tool 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 schema already provides 100% coverage for both parameters (type and values), so the baseline is 3. The description adds valuable meaning by specifying what data each enum value returns ('type="company" pulls LATEST 10-K revenue...', 'type="drug" pulls FAERS adverse-event counts...') and clarifies accepted input formats (tickers/CIKs vs names). This enriches the enum semantics beyond the schema's terse descriptions.

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 clear trigger phrases ('Compare X and Y', 'X vs Y', 'rank these companies') and explicitly states the core function: 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It distinguishes itself from sibling tools by positioning as the preferred alternative to sequential single-pack lookups and mentions it replaces 8–15 lookups.

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 via example query patterns and states 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It also clarifies the difference between type='company' and type='drug', helping the agent choose the correct variant based on the entities being compared. This provides clear usage context and an explicit alternative to avoid.

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