open_payments_top_doctors

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds useful behavioral context beyond that: it aggregates all individual payments per doctor, groups by doctor, and sorts by total descending. This clarifies the data transformation the tool performs, which is valuable for an agent deciding between this and other payment tools.

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?

Three sentences, each immediately useful: the goal, the aggregation algorithm, and the primary use case. It is front-loaded with the most important action. The last sentence is slightly promotional but still helps usage selection. No wasted sentences or redundant schema repetition.

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 five-parameter aggregate tool with no output schema, the description gives a solid conceptual model but does not explain whether state/company/specialty filters are applied before aggregation, nor describe the returned fields. It is adequate but has clear gaps around filter semantics and output shape.

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 description coverage is 100%, so the structured schema already explains year, limit, state, company, and specialty. The description adds only the conceptual ranking/filtering context and does not explain how each parameter interacts with the aggregation. Baseline 3 applies as the schema carries the parameter-heavy lifting.

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 a specific verb and resource: 'Find the HIGHEST-PAID doctors by TOTAL payments received', and explains the aggregation logic (groups by doctor, sums payments, sorts descending). It succeeds in distinguishing this from lower-level search tools like open_payments_search, though it does not explicitly call out sibling alternatives such as open_payments_top or open_payments_by_physician.

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 clear context for when to select this tool: 'key tool for finding doctors with the biggest pharma relationships.' This orients an agent to the intended use case. However, it does not mention when not to use it or name specific alternatives, leaving some routing ambiguity among the many open_payments_* siblings.

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