Add it to Claude Code
claude mcp add -e "DATABASE_URL=${DATABASE_URL}" postgres-safe -- npx -y pg-redact-mcpDATABASE_URLMake your agent remember this setup
postgres-safe's config, env vars, and the gotchas you hit — recalled in every future Claude Code, Cursor, and Codex session.
npx conare@latestFree · one command · indexes the sessions already on disk. Set up in the browser instead →
What it does
- Automatic PII detection using column-name heuristics and Microsoft Presidio NLP
- Masking by default for sensitive data like emails, names, and SSNs
- Selective unmasking capabilities via AI tool parameters
- Hard-coded protection for secret columns like passwords and tokens
- Human-in-the-loop approval for unmasking requests
Tools 3
queryExecutes a SQL query against the database with automatic PII masking applied to the results.describe_schemaRetrieves the database schema to help the AI understand table and column structures.configure_maskingUpdates masking rules in-memory for the current session.Environment Variables
DATABASE_URLrequiredThe connection string for the PostgreSQL database.Try it
Original README from sam-david/pg-redact-mcp
postgres-safe-mcp
A PostgreSQL MCP server that automatically detects and obfuscates PII in query results before they reach the AI. Connect Claude (or any MCP client) to your database without exposing sensitive data.
How it works
- Connect — Point the server at any PostgreSQL database (local or remote)
- Schema check — The agent calls
describe_schemafirst to learn the actual table and column names, avoiding guesswork and failed queries - Auto-detect — On startup, the server runs instant heuristic detection on all column names to classify PII (emails, names, phones, SSNs, etc.). No data sampling required — column name patterns catch the vast majority of PII fields
- Mask by default — Query results are automatically masked before the AI sees them:
PII masked: first_name (MASKED: PERSON), last_name (MASKED: PERSON), email (MASKED: EMAIL_ADDRESS) 3 rows | id | first_name | last_name | email | created_at | |------|------------|-----------|---------------|------------| | 5022 | S** | D**** | s***@g***.com | 2026-03-06 | | 5021 | S** | D**** | s***@g***.com | 2026-03-03 | | 5020 | l*** | c*** | l***@t***.com | 2026-02-24 | - Reveal when needed — The AI can selectively unmask specific columns or PII types when the user explicitly asks to see real data
Masking and unmasking
Default behavior: everything masked
Every query runs through the redaction engine before results reach the AI. PII columns are detected by name pattern and masked automatically. The AI sees partial values like J****** instead of Jessica — enough structure to reason about the data without exposing real PII.
How the AI decides what to reveal
The query tool accepts two optional parameters for selective unmasking:
reveal_columns— Unmask specific columns by name (e.g.["email", "first_name"])reveal_types— Unmask all columns of a PII type (e.g.["EMAIL_ADDRESS"])
The AI is guided by these rules in the tool description:
| Scenario | What the AI does |
|---|---|
| "Show me the last 5 registrations" | Keeps everything masked — browsing doesn't need real data |
| "How many users signed up last month?" | Aggregate query, no PII in results |
| "What is the email for registration 5015?" | Reveals email — user explicitly asked for it |
| "Show me John's full name" | Reveals first_name, last_name — user asked to see the value |
| "Are there duplicate registrations?" | Keeps masked — duplicates are detectable from masked patterns |
Secret columns can never be revealed
Columns matching patterns like encrypted_password, otp_secret_key, reset_password_token are classified as SECRET. These are always fully redacted ([REDACTED]) and cannot be unmasked even if reveal_columns or reveal_types is used. There's no legitimate reason for an AI agent to see raw password hashes or auth tokens.
Manual overrides
You can force specific masking behavior per column in your config:
column_rules:
# Force a column to be treated as PII even if the name doesn't match patterns
- table: users
column: custom_id_field
pii_type: US_SSN
masking_style: partial
# Explicitly mark a column as NOT PII (skip masking)
- table: users
column: display_name # public-facing, not sensitive
pii_type: none
masking_style: none
Or at runtime via the configure_masking tool (in-memory, not persisted).
Human in the loop
In Claude Code, every tool call is shown to the user before execution. When the AI uses reveal_columns, you see exactly which columns are being unmasked and can approve or deny the request. This creates a natural checkpoint — the AI proposes what to reveal, you decide whether to allow it.
PII detection
Detection uses a two-layer approach:
- Column name heuristics (fast, no NLP) — Pattern matching on column names handles common patterns like
email,first_name,phone, plus prefixed variants likebus_email,rep_phone_number,pref_first_name,former_last_name - Presidio NLP analysis (on first access) — Samples ~100 rows and runs Microsoft Presidio to detect PII in column values, catching columns with non-obvious names
Detected PII types include: email addresses, phone numbers, names, physical addresses, SSNs, tax IDs, credit cards, IP addresses, dates of birth, financial account numbers, geolocation, and more.
Secret columns (encrypted passwords, tokens, OTP secrets) are always fully redacted and cannot be revealed.
Free text columns (message bodies, notes, descriptions) get value-level Presidio scanning since PII is embedded in prose.
Masking styles
| Style | Example | Description |
|---|---|---|
partial (default) |
j***@e***.com |
Shows enough structure to be useful, hides the sensitive parts |
full |
[EMAIL ADDRESS] |
Complete replacement with a type label |
pseudonymize |
`user_a3f2@ |