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

Annotations already mark this as read-only, idempotent, and non-destructive. The description adds meaningful context beyond those annotations: what the tool returns, that it contains no affiliate links, and a clear call-to-action for sensitive health information. This transparency is especially valuable for a health-related data tool and does not contradict any annotation.

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 front-loaded with purpose and output, then adds transparency and usage constraints. While it is 6 sentences, each sentence adds necessary information, and the safety warning is appropriately explicit. No filler or redundancy is present.

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 provides a clear scope and output list, and the output schema likely covers return values. However, with 6 parameters and no schema descriptions, the description does not fully compensate for the missing parameter semantics. It also does not explicitly differentiate from sibling tools like search_evidence, leaving some ambiguity about when this specific dataset map is the right choice.

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 has 6 parameters with 0% description coverage, and the tool description does not explain any of them. The only hint is 'filtered rows,' which implies parameters serve as filters but omits details on how each parameter maps to fields or how to use them. Since the description must compensate for the schema gap and does not, this dimension is under-served.

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 ('Query') and a specific resource ('DOI-backed HAA Supplement Evidence Density Map 2026'), and lists concrete return items (dataset version, denominator, methods, citation, filtered rows). This clearly distinguishes it from sibling tools like search_evidence or get_evidence_summary, which target broader or different evidence functions.

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 constraints on when to use the tool: 'Use only for public, non-personal evidence questions' and explicitly lists what not to do for personal/sensitive health information. It does not name alternative tools, but the exclusions are unambiguous, and it provides user-facing guidance for out-of-scope requests.

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