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Oig Cia Check

oig_cia_check
Read-onlyIdempotent

Does this healthcare company or individual have an HHS-OIG Corporate Integrity Agreement (CIA)? Checks a name against the current HHS-OIG CIA roster — companies and individuals OIG agreed not to exclude from Medicare/Medicaid in exchange for binding compliance obligations, as part of a False Claims Act or other settlement. Different from an EXCLUSION (see the leie / ny-omig-exclusions packs): a CIA entity is still participating, under obligations. An exact name match is the strongest signal; a normalized-name or partial/token match is returned as a labelled CANDIDATE, since CIA entity names are often compound (a company plus named co-signers) — e.g. searching "SNAP Diagnostics" finds "SNAP Diagnostics, LLC and Gil Raviv" as a candidate. Returns, for the best match: status (Effective/Suspended/Closed/Refused), effective date, term/duration, settlement amount, and the agreement PDF URL (fetched live from the entity's own detail page — not stale). Returns found:false with the list's as-of date and total agreement count when no match exists. Sourced from HHS-OIG, keyless.

Input Schema

NameRequiredDescriptionDefault
nameYesCompany or individual name to check, e.g. "SNAP Diagnostics" or "Alliance Parent, Inc.". Any word order; partial names are matched as candidates.
limitNoMaximum candidates to return (default 5, max 20). Only the top/best candidate is enriched with live detail-page data.

Schema Changelog

Changes observed during successful MCP inspections.

No schema history has been recorded yet.

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already cover read-only/idempotent safety, and the description adds real behavioral context beyond them: live-fetched PDF URL (not stale), found:false behavior with as-of date and total count, and candidate labelling for fuzzy matches. Only minor gap is no mention of latency or how many lookups the live fetch costs.

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?

Dense but front-loaded: the core question comes first, then the exclusion contrast, then matching semantics, then return shape. A few clauses are repeated across sentences (the CANDIDATE explanation and the SNAP example), costing one point.

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

Completeness5/5

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

No output schema exists, and the description fully covers the return contract: status enum values, effective date, term, settlement amount, PDF URL, and the found:false payload with as-of date and total count. Nothing material is missing for correct invocation.

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?

Schema coverage is 100%, so baseline is 3; the description earns above that by explaining matching semantics — 'any word order; partial names are matched as candidates', compound entity names like 'SNAP Diagnostics, LLC and Gil Raviv', and that only the best candidate gets live enrichment.

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?

Opens with a precise question-form verb+resource ('Does this ... have an HHS-OIG Corporate Integrity Agreement') and immediately distinguishes itself from the sibling packs (oig_cia_list, oig_cia_obligations) and from exclusion checks. An agent knows exactly what decision this tool answers.

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?

Explicitly states when to use this versus alternatives: 'Different from an EXCLUSION (see the leie / ny-omig-exclusions packs)' and explains that a CIA entity is still participating under obligations. It also tells the agent how to interpret partial matches (CANDIDATE) versus exact matches.

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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