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lzinga

US Government Open Data MCP

by lzinga

open_payments_summary

Read-onlyIdempotent

Retrieve national totals and averages of pharmaceutical payments to doctors for all years, showing aggregate industry-to-physician financial flows.

Instructions

Get national-level Open Payment totals and averages across all years. Shows how much money flows from pharma to doctors nationally.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv2026.4.11
    • removedInput schema / additionalProperties
      Removed value: -false
  2. First observedv2026.3.9

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds behavioral context by specifying the scope (national, all years) and the nature of the data (totals and averages). It does not describe output format or potential limitations, but given the annotations and simple summary nature, it provides adequate transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the primary purpose, and no redundant information. Every word earns its place, making it highly efficient and clear.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a no-parameter, read-only summary tool, the description covers the key aspects: what it does, the scope, and the type of data returned. It lacks explicit mention of output structure or field names, but since there is no output schema, this is a minor gap. The tool is simple enough that an agent can infer expected results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so the baseline is 4. The description adds no parameter information because none exist; it is not required.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves national-level Open Payment totals and averages across all years, and explains its significance (money flow from pharma to doctors). It is specific and distinguishes this from sibling tools by emphasizing 'national-level' and 'across all years', differentiating it from more granular tools like open_payments_by_company 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.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for a national summary but does not explicitly state when to use this tool versus alternatives, nor does it mention any exclusions. With many Open Payments sibling tools, explicit routing guidance would be helpful, but the national scope provides some implied context.

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

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