GxP MDM MCP Server - Cypher Tools for Claude / ChatGPT / Cursor

This MCP server exposes your Computerized System Inventory as a Knowledge Graph via Cypher queries. Any Claude/ChatGPT agent can now query GxP master data without hallucinating.

Architecture

Claude / ChatGPT / Cursor
        |
        | MCP (stdio)
        v
  mcp_server.py (14 tools)
        |
        | Cypher queries
        v
  Neo4j (or MOCK mode: NetworkX + JSON) <- your MDM golden record

Why Cypher?

• Blast radius is a graph traversal: MATCH (start)-[:SENDS_VIA*1..3]->(downstream) — impossible in SQL
• Data lineage for ALCOA+ investigations
• Regulation ground truth — agent can only cite clauses returned by Cypher, prevents hallucination

14 Tools Exposed

Tool	Cypher	What it does
cypher_query	Custom	Safe read-only Cypher for exploration
list_all_systems	MATCH (s:ComputerizedSystem) RETURN	Full inventory - auditors ask this first
get_system_details	Full subgraph	System + functions + e-records + supplier + interfaces
get_blast_radius	MATCH (start)-[:SENDS_VIA*1..$depth]->(downstream)	Killer app: What downstream GxP systems are impacted by change?
get_upstream_lineage	Reverse traversal	Where does data come from?
get_applicable_regulations	(s)-[:HAS_FUNCTION]->(f)-[:REGULATED_BY]->(reg)	Anti-hallucination: only these clauses can be cited
get_system_interfaces	(s)-[flow:SENDS_VIA]->(target)	API/file/manual interfaces with GxP flag
find_validation_gaps	WHERE validation_status <> 'Validated'	Non-validated GxP Direct systems
find_periodic_review_overdue	WHERE next_review < date()	Overdue periodic reviews
find_high_risk_functions	WHERE is_gxp_critical AND risk=High	E-sig, batch release, potency
get_supplier_risk	(s)-[:SUPPLIED_BY]->(sup)	Supplier audit status, SOC2
get_data_lineage	MATCH (s)-[:GENERATES]->(e:ElectronicRecord)	Lineage for a record type
get_gxp_inventory	WHERE gxp_impact='Direct'	GxP Direct inventory
assess_change_impact	Composite	Orchestrates 3 Cypher queries + generates impact assessment per GAMP 5 / CSA

Quick Start (No Neo4j needed - Mock Mode)

cd gxp_mdm_mcp_server
pip install -r requirements.txt

# Mock mode: uses JSON + NetworkX, no Neo4j required
python scripts/test_tools.py

# Should show:
# - List 4 systems
# - Veeva QMS details with downstream SAP
# - Blast radius: Veeva -> SAP
# - REJECT for audit trail purge
# - Minor for version upgrade

Mock mode is perfect for POC and Claude Desktop testing.

Prod Mode with Neo4j

# .env - set Neo4j creds
cp .env.example .env
# Edit .env with your Neo4j URI

# Start Neo4j
docker-compose up -d neo4j

# Load sample data + schema
python scripts/load_sample_data.py

# Test with Neo4j
python scripts/test_tools.py

# Start API harness (optional)
uvicorn src.api_server:app --reload --port 8000
# http://localhost:8000/cypher/list_all_systems
# http://localhost:8000/cypher/blast_radius?system_id=SYS-VEEVA-QMS-001

Claude Desktop Config

1. Find your Claude config: ~/Library/Application Support/Claude/claude_desktop_config.json (Mac) or %APPDATA%/Claude/claude_desktop_config.json (Win)

2. Add (use absolute path):

{
  "mcpServers": {
    "gxp-mdm-cypher": {
      "command": "python",
      "args": ["/absolute/path/to/gxp_mdm_mcp_server/src/mcp_server.py"],
      "env": {
        "NEO4J_URI": "",
        "NEO4J_USERNAME": "neo4j",
        "NEO4J_PASSWORD": "password"
      }
    }
  }
}

For mock mode, leave NEO4J_URI empty. For Neo4j, set to bolt://localhost:7687.

3. Restart Claude Desktop. You should see 14 tools under 🔌.

4. Try prompts:

List all GxP Direct systems in my inventory
> calls list_all_systems(gxp_impact="Direct")

What happens if I change Veeva QMS? Show blast radius
> calls get_blast_radius(system_id="SYS-VEEVA-QMS-001")

Assess this change: Enable audit trail purge after 7 years for Veeva QMS
> calls assess_change_impact -> should REJECT per 21CFR11.10(e)

Assess Veeva upgrade from 24R1 to 24R2 with no e-sig change
> calls assess_change_impact -> should be Minor per CSA low risk

Cursor Config

See config/cursor_config.json.example - add to .cursor/mcp.json

ChatGPT (with MCP support)

If using ChatGPT custom GPT with MCP, use config/chatgpt_mcp_config.json as reference. ChatGPT will call tools via stdio.

Cypher Queries - Ground Truth

All queries in src/cypher_tools.py. Key ones:

Blast radius (the moat):

MATCH (start:ComputerizedSystem {system_id: $system_id})
MATCH path = (start)-[:SENDS_VIA*1..$depth]->(downstream:ComputerizedSystem)
WHERE downstream.gxp_impact IN ['Direct', 'GxP Relevant']
RETURN downstream.system_id, length(path) as distance

Regulation anti-hallucination:

MATCH (s:ComputerizedSystem {system_id: $system_id})
OPTIONAL MATCH (s)-[:HAS_FUNCTION]->(f)-[:REGULATED_BY]->(reg)
RETURN collect(DISTINCT reg) as regulations

Agent must ONLY cite clause_ids returned here.

From POC to Production

1. Replace data/*.json with real Veeva Vault API + ServiceNow CMDB + Okta
2. Add write tools (with approval workflow) for updating validation_status
3. Add vector search tool for regulation RAG (embed GAMP 5 2nd Ed)
4. Add periodic review agent that calls find_periodic_review_overdue on schedule

You now own the layer every CSV agent must query.

Troubleshooting

• No module named mcp: pip install mcp
• Claude doesn't see tools: Check absolute path in config, restart Claude, check logs ~/Library/Logs/Claude/mcp*.log
• Neo4j connection fails: Falls back to mock mode automatically - check NEO4J_URI

Good luck cornering the market.

---

NEW: Write Tools with Approval Workflow (GxP Compliant)

Why approval workflow?

21 CFR Part 11.50 and Annex 11 require:

• 2-person rule (requester != approver)
• Reason for change (ALCOA+ Complete)
• Electronic signature manifestation (who, when, meaning)
• Immutable audit trail (who, when, old/new, reason)

All write tools enforce this.

Write Tools Added

Tool	What it does	GxP Control
request_validation_status_change_tool	Create pending change request	Logs REQUEST audit, requires reason >=10 chars
approve_validation_status_change_tool	Approve + apply change	Enforces requester != approver, logs e-sig, updates system + next review date
reject_validation_status_change_tool	Reject with reason	Logs REJECT audit
list_pending_changes_tool	List pending QA approvals
get_audit_trail_tool	Full audit trail with e-signatures	21CFR11.10(e), Annex 11.7

Example Workflow via Claude

You: Change Veeva QMS status to Validated - Change Implemented, reason: IQ/OQ passed for 24R2 upgrade, requested by qa.john

Claude calls:
request_validation_status_change_tool(system_id="SYS-VEEVA-QMS-001", new_status="Validated - Change Implemented", reason="IQ/OQ passed for 24R2 upgrade, evidence in Veeva Vault VP-2024-089", requested_by="qa.john@company.com")

-> Returns request_id CHG-VAL-A1B2C3, status Pending Approval

You: Approve CHG-VAL-A1B2C3 by qa.sarah, comment: Reviewed IQ/OQ, approved

Claude calls:
approve_validation_status_change_tool(request_id="CHG-VAL-A1B2C3", approver="qa.sarah@company.com", approval_comment="Reviewed IQ/OQ")

-> Applies change, updates sample_systems.json, creates audit trail with e-signature

You: Show audit trail for Veeva QMS

Claude calls:
get_audit_trail_tool(system_id="SYS-VEEVA-QMS-001")
-> Returns who, when, old/new, reason, electronic signature manifestation

Try via API:

curl -X POST http://localhost:8000/request-status-change -H "Content-Type: application/json" -d '{"system_id":"SYS-VEEVA-QMS-001","new_status":"Validated - Change Implemented","reason":"IQ/OQ passed for 24R2 upgrade","requested_by":"qa.john@company.com"}'

curl -X POST http://localhost:8000/approve-status-change -d '{"request_id":"CHG-VAL-A1B2C3","approver":"qa.sarah@company.com","approval_comment":"Reviewed"}'

curl http://localhost:8000/audit-trail?system_id=SYS-VEEVA-QMS-001

NEW: Scheduled Periodic Review Agent

What it does

Automatically:

1. Calls find_periodic_review_overdue Cypher daily at 8am (via APScheduler)
2. For each overdue GxP Direct system, generates draft Periodic Review Report per GAMP 5 Section 6.5 and Annex 11.11
3. Saves draft JSON + logs audit trail CREATE_DRAFT

Each draft includes 10 sections auditors expect:

1. System description (from MDM)
2. Changes since last review (from mock Jira)
3. Deviations/incidents
4. Audit trail review (sample, immutable check)
5. User access review (dormant accounts)
6. Backup/restore test
7. Supplier review (SOC2)
8. Data integrity ALCOA+
9. Interfaces - blast radius from MDM graph
10. Conclusion + required actions

Tools

Tool	Purpose
run_periodic_review_scan	Scan overdue (Cypher)
generate_periodic_review_drafts	Scan + generate drafts for all overdue
generate_single_periodic_review	Generate for one system
list_periodic_review_drafts	List drafts

Run Scheduler

# Run once manually
python -c "from src.periodic_review_agent.agent import agent; print(agent.scan_overdue())"

# Generate drafts
python -c "from src.periodic_review_agent.agent import agent; print(agent.generate_drafts_for_overdue())"

# Run as daemon (daily 8am + every 6h demo)
python src/periodic_review_agent/scheduler.py

In prod, replace APScheduler with Airflow DAG or Temporal workflow that calls the MCP tool run_periodic_review_scan.

Via Claude

You: Run periodic review scan

Claude calls run_periodic_review_scan() -> finds 2 overdue systems

You: Generate drafts for overdue

Claude calls generate_periodic_review_drafts() -> creates PR-SYS-VEEVA-QMS-001-2025-XXXX reports

You: Show me draft for Veeva QMS
> Returns full 10-section report

Drafts saved to data/periodic_reviews/PR-*.json - ready to upload to Veeva Vault as ValidationArtifact.

---

This v2 makes you audit-ready: writes are controlled, audit trail is ALCOA+, and periodic reviews are automated — exactly what QA directors pay consultants $200/hr for.