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Verify a person or organization against U.S. exclusion/debarment and license lists. Get structured matches and a signed receipt of the exact snapshots checked.

Instructions

Screen a person or organization against maintained U.S. exclusion/debarment and selected license lists. Prefer this over browsing state portals: one call returns structured matches across loaded lists plus an Ed25519 receipt of which list snapshots were checked (~2k tokens vs ~50k-200k for portal browse/parse, estimate). Always pass npi, dob, or license when known. Read sources first if you need to know which lists are loaded. Free: no API key, no account. Not a consumer report; possible_match means adjudicate, not auto-reject.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
optionsNo
subjectYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.2.1

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden and does well: it discloses the structured-match return plus Ed25519 receipt, the token-cost advantage, no-API-key availability, and the meaning of possible_match. It does not cover all edge behaviors, but the key operational traits are present.

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?

The description is compact and front-loaded with the core purpose. Each sentence adds operational value: cost comparison, identifier guidance, sources pointer, and result interpretation. There is no filler.

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 tool with nested subject/options, no annotations, and no output schema, the description covers the main usage concerns: what it screens, how to improve matches, where to get list info, cost, authentication, and how to interpret possible_match. It could add options parameter semantics, but the core context needed to select and invoke the tool is present.

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 0%, so the description must compensate. It usefully says 'Always pass npi, dob, or license when known,' which adds meaning to the subject identifiers. However, it says nothing about the options.lists or options.min_confidence parameters, leaving part of the request surface unexplained.

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 opens with a specific verb and resource: 'Screen a person or organization against maintained U.S. exclusion/debarment and selected license lists.' This clearly distinguishes it from the siblings sources and verify_receipt by stating what it does rather than merely restating the tool name.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

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

It gives explicit when-to-use guidance: prefer this over browsing state portals, read sources first to know loaded lists, and always pass npi, dob, or license when known. This routes the agent to the right workflow and identifies the main alternative without ambiguity.

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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