Find total payments each physician received from pharmaceutical companies, with totals pre-aggregated across all years. Enter a limit to view a specific number of doctors.
Get payments grouped by individual physician across all years. Pre-aggregated totals — shows how much each doctor received from pharma overall.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of physicians (default 20) |
Changes observed during successful MCP inspections.
v2026.9.14Input schema / properties / limit / defaultAdded value: +20v2026.4.11Input schema / additionalPropertiesRemoved value: -falsev2026.3.9Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the description doesn't need to repeat those. It adds context about pre-aggregation and the all-years scope, which goes beyond the schema. It doesn't mention pagination or response structure, but for a simple aggregate with a single limit parameter, this is adequate.
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?
Two short sentences with zero filler. The core purpose is front-loaded, and the second sentence clarifies the output nature. Every word earns its place.
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 one parameter and no output schema, the description adequately conveys what the tool returns (physician-level totals). It could mention that the response is a list of physicians with amounts, but given the pre-aggregated nature and the sibling tools, it's sufficiently complete for an agent to call 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?
Schema coverage is 100% for the only parameter (limit), with a clear description and default/maximum. The description doesn't add extra semantics to the limit parameter, but it does provide context that results are grouped and aggregated. Baseline 3 is appropriate when the schema already documents the parameter fully.
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?
States a specific verb (Get) and resource (payments grouped by individual physician), and explicitly notes it's pre-aggregated totals. This distinguishes it from siblings like open_payments_by_company, by_hospital, and by_specialty, which group by different entities. The description makes the tool's intent unambiguous.
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 clarifies that results are pre-aggregated across all years, implying this is for overall physician totals rather than detailed payment records. It doesn't explicitly mention alternatives like open_payments_search for granular data or open_payments_top_doctors for ranked lists, but the grouping and aggregation scope are clear enough to infer when to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.