Healthcare Data Quality MCP Server

An MCP (Model Context Protocol) server that exposes healthcare data quality validation as tools any MCP-compatible AI client — Claude Desktop, Claude Code, or Cowork — can call directly in conversation.

This wraps the rule logic from the Healthcare Data Quality & Governance Agent skill — all four quality dimensions (completeness, integrity, consistency, temporal accuracy) across five CMS/claims-adjacent data types — as callable tools, so instead of pasting data into a chat and asking for a report, an AI assistant can run the checks itself against a real file.

The rule logic and severity scoring live here as tested, versioned code. Turning the structured findings into the CDO-facing governance report (plain-English explanations, executive summary, PDF) stays a job for the skill/prompt layer, which consumes this server's JSON output.

Supported data types

inpatient, outpatient, carrier, pharmacy, ehr, population_health — see rules/schema.py for the exact required fields and identifiers per type.

Related MCP server: Delpha Data Quality MCP

What it does

• profile_dataset — row/column counts, columns detected, sample rows, and a best-guess data type (confirm before running checks)

• check_completeness — flags required fields that are null, blank, or placeholder values, with severity scoring (Critical / High / Medium / Note)

• check_integrity — flags duplicate claim IDs, malformed provider NPIs, invalid ICD-9/ICD-10 diagnosis code formats, invalid beneficiary IDs, and payment amount anomalies

• check_consistency — flags mixed date formats and CMS code-set violations (gender, race, state FIPS, claim type, chronic condition flags)

• check_temporal — flags illogical date sequences (discharge before admission, service after death), future-dated records, implausible ages, and length-of-stay anomalies

• run_full_quality_scan — runs all four dimensions and returns a severity-scored, prioritised summary with AI readiness signals

Every dimension tool (except profile_dataset) takes filename and data_type.

Setup

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Run it standalone (no MCP client needed)

python3 -c "
import rules, json
df = rules.load_csv('sample_data/claims.csv')
print(json.dumps(rules.run_full_scan(df, 'outpatient'), indent=2))
"

Other sample files: inpatient_sample.csv, carrier_sample.csv, pharmacy_sample.csv, ehr_sample.csv, population_health_sample.csv — each paired with its matching data type.

Connect it to Claude Desktop

1. Open your Claude Desktop config file (claude_desktop_config.json).

2. Add an entry like the one in claude_desktop_config.example.json, updating the path to the absolute path of server.py on your machine.

3. Restart Claude Desktop.

4. Ask Claude something like: "Run a full quality scan on claims.csv as outpatient claims data" — Claude will call the tool, run the checks, and summarize the findings.

Notes on the SDK

This was built against mcp==2.0.0, which uses mcp.server.MCPServer as the high-level server class. Some older tutorials reference mcp.server.fastmcp.FastMCP under the same name — the @mcp.tool() decorator API is identical either way; only the import path and class name differ by SDK version.