phi-guard-mcp
MCP server and CLI for detecting, redacting, and auditing PHI before medical text is sent to AI agents.
phi-guard-mcp is healthcare AI safety infrastructure, not a clinical product. It is a local,
rule-based guardrail that helps developers identify PHI-like identifiers in plain text, redact them
with stable placeholders, and produce audit-friendly JSON before content reaches an AI agent or MCP
workflow.
Proof points for maintainers:
- Synthetic benchmark with exact-match PHI finding evaluation.
- Safe Harbor mapping audit fields for review workflows.
- CI privacy gate that blocks PHI-like identifiers in maintained source and docs.
- CLI, Python API, and MCP stdio tools sharing one stable JSON result model.
Important scope limits:
- Not for diagnosis, treatment, triage, medical advice, or medication recommendations.
- Not a HIPAA compliance guarantee and not a substitute for legal, privacy, or security review.
- Not an FDA-regulated clinical decision support or device software function.
- Do not test with real patient records. The examples in this repo are synthetic.
The project aligns its documentation vocabulary with HHS HIPAA de-identification concepts such as Safe Harbor and Expert Determination, while intentionally avoiding clinical decision support scope. See HHS de-identification guidance, FDA CDS guidance, and FDA device software functions.
Install
Install from the current GitHub release wheel:
python -m pip install https://github.com/charlesree826/phi-guard-mcp/releases/download/v0.1.2/phi_guard_mcp-0.1.2-py3-none-any.whl
For local development:
python -m pip install -e ".[dev]"
PyPI publishing is configured through GitHub Actions trusted publishing and will be enabled after the PyPI package owner creates the matching pending publisher entry for this repository.
Quickstart
Scan a synthetic note:
phi-guard scan examples/synthetic_clinical_note.txt
Redact PHI-like identifiers:
phi-guard redact examples/synthetic_clinical_note.txt --out /tmp/synthetic_redacted.txt
Audit a note:
phi-guard audit examples/synthetic_clinical_note.txt
Validate text before it enters an AI agent:
phi-guard validate examples/synthetic_clean_note.txt
Run the synthetic benchmark:
phi-guard benchmark benchmarks/synthetic/cases --out benchmarks/synthetic-report.json
Run the repository privacy gate:
phi-guard gate --config .phi-guard.toml
All CLI commands output stable JSON for automation.
See docs/demo.md for a complete CLI and MCP transcript.
MCP Server
Run the stdio MCP server:
phi-guard-mcp
Available tools:
scan_phi(text)redact_phi(text, mode="placeholder")audit_deidentification(text)validate_no_phi(text)
MCP tools return the same finding schema as the CLI, including safe_harbor_identifier.
Example MCP client config:
{
"mcpServers": {
"phi-guard": {
"command": "phi-guard-mcp"
}
}
}
Python API
from phi_guard_mcp import audit_text, evaluate_benchmark, redact_text, scan_text, validate_no_phi
result = scan_text("Patient Name: Jordan Rivera, MRN: MRN-48291")
redacted = redact_text("Patient Name: Jordan Rivera, MRN: MRN-48291")
audit = audit_text("Patient Name: Jordan Rivera, MRN: MRN-48291")
validation = validate_no_phi("No identifiers are present in this synthetic note.")
benchmark = evaluate_benchmark("benchmarks/synthetic/cases")
What It Detects
The first release focuses on plain text and common PHI-like identifiers:
- Names in clinical label contexts
- Dates
- Phone numbers
- Email addresses
- Address-like fragments
- Medical record numbers
- Social Security numbers
- URLs and IP addresses
- Medical facility names
- Account, member, policy, and patient ID tokens
This is a deterministic heuristic engine. It favors transparent behavior and repeatable JSON over opaque model judgment.
Safe Harbor mapping is included as a review aid only. It does not make output HIPAA compliant and does not replace Expert Determination or legal review.
Project Docs
Development
python -m compileall -q src tests
python -m pytest -q
ruff check .
phi-guard gate --config .phi-guard.toml
python -m build
twine check dist/*
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