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lzinga

US Government Open Data MCP

by lzinga

open_payments_search

Read-onlyIdempotent

Find payments from pharmaceutical and device companies to physicians in CMS Open Payments data. Filter by doctor, company, specialty, or state to see amounts, payment types, and associated drugs or devices.

Instructions

Search CMS Open Payments (Sunshine Act) data — payments from pharma/device companies to doctors. 15M+ records per year. Shows exact dollar amounts, payment type, doctor name/specialty, and which drugs/devices are involved. Cross-reference with FDA (drug safety), lobbying (company influence), and clinical trials.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNoPayment year (auto-discovers latest if omitted). Available: 2018-2024+, new years added automatically when CMS publishes.
limitNoMax results (default 20)
stateNoTwo-letter state: 'CA', 'TX', 'NY'
doctorNoDoctor last name: 'Smith', 'Jones' (case-insensitive)
companyNoCompany name (partial match): 'Pfizer', 'Novo Nordisk', 'Johnson & Johnson'
specialtyNoMedical specialty (partial match): 'Cardiology', 'Orthopedic', 'Psychiatry'

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv2026.9.14
    • addedInput schema / properties / limit / default
      Added value: +20
  2. Changed1 schema field changedv2026.4.11
    • removedInput schema / additionalProperties
      Removed value: -false
  3. First observedv2026.3.9

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering the core safety profile. The description adds context about data scale ('15M+ records per year') and output content, which is useful, but it does not disclose further behavioral details such as pagination, result ordering, or how parameters interact beyond what the schema already provides.

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

Conciseness4/5

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

The description is front-loaded with the core purpose and is reasonably compact at three sentences. The cross-reference sentence is somewhat tangential to the tool's direct behavior but still adds useful context without bloat.

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

Completeness3/5

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

For a search tool with 6 optional parameters, no output schema, and a large set of sibling Open Payments tools, the description provides enough to understand general search behavior, but it does not explicitly address how it relates to the sibling open_payments_* tools or what the default/result shape is. This leaves some ambiguity for an agent selecting among similar tools.

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

Parameters3/5

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

Schema description coverage is 100%, with every parameter already explained in the input schema. The description adds no additional parameter semantics or syntax details; the baseline of 3 applies because the schema carries the full burden.

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

Purpose4/5

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

The description clearly identifies a specific resource ('CMS Open Payments (Sunshine Act) data') and action ('Search') and enumerates key output fields ('dollar amounts, payment type, doctor name/specialty, and which drugs/devices are involved'). It does not explicitly differentiate itself from the sibling open_payments_* tools, such as open_payments_research or open_payments_by_company, so it stops short of full differentiation.

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 a general search use case and suggests cross-referencing with FDA, lobbying, and clinical trials data, giving some context for when an agent might use it. It does not explicitly state when to prefer this tool over the other open_payments_* siblings or provide exclusions or alternative routing guidance.

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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