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Privacyscrubber MCP

by moxno

Locally masks PII, secrets, and credentials before LLM use, then restores approved tokens in the local session.

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Install Privacyscrubber MCP

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "moxno-privacyscrubber-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@privacyscrubber/mcp-server"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Install not yet sandbox-tested— the server itself is confirmed live (we called its tools/list endpoint). The stdio install command is a separate, ongoing check.

Overview

moxno/privacyscrubber-mcp provides local PII, secret, and credential masking for MCP clients including Cursor, Windsurf, and Claude Desktop. It replaces detected values with tokens before content reaches an LLM and can restore those values from an in-memory session map afterward. Tools cover text, files, shell output, Git diffs, patches, RAG chunks, audits, and compliance logs. Reach for it when an agent needs to work with sensitive developer or business data without exposing the original values to a remote model.

Use cases

  • Mask credentials in source files before sending them to an AI coding assistant
  • Inspect command output for secrets before agent processing
  • Sanitize document chunks before embedding them in a vector database
  • Generate compliance evidence from an MCP session
  • Redact sensitive local files while retaining backup copies

Key features

  • Local tokenization of PII, secrets, and credentials
  • Reversible in-memory masking and restoration
  • File, directory, shell, and Git diff protection
  • RAG chunk sanitization and restoration
  • Compliance reports and SIEM-format audit exports
  • Workspace configuration and agent-rule scaffolding

Tools23

What Privacyscrubber MCP lets your agent do. Expand a tool for its parameters.

Verified live~3.6k tokens of context

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 2d ago

Recent health check succeeded.

18/23 checks healthy over the last 83d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 12d ago via OSV.dev · @privacyscrubber/mcp-server (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars3Stargazers on the source repository.
Last commit2d agoMost recent push to the default branch.
Tools exposed23Callable tools this server registers over MCP.
Directory activity9 viewsConfig copies, upvotes, and views on AllMCPs.

How Privacyscrubber MCP works

What moxno/privacyscrubber-mcp does

moxno/privacyscrubber-mcp adds a local privacy boundary between MCP clients and remote language models. It detects PII, secrets, credentials, and configurable patterns in prompts, files, command output, source changes, and document chunks. Detected values are replaced with labels such as [EMAIL_1] or [API_KEY_1], allowing an agent to process the surrounding content without receiving the original values.

The server also supports inspection and governance workflows. It can classify input as CLEAN, LOW, MODERATE, or CRITICAL, identify applicable frameworks including GDPR, HIPAA, SOC 2, and PCI DSS, scan directories for exposed data, create compliance reports, and export tamper-evident audit logs in CEF, RFC 5424 Syslog, JSONL, or JSON formats.

How it works

A typical exchange has three stages. First, audit_context can inspect raw content without changing it. Next, sanitize_text, sanitize_file, or a Guard tool replaces sensitive values and retains the mapping in a local volatile-memory session. After the model produces an answer, reveal_text can substitute the tokens with their original values before the result is shown locally.

The same token flow applies to agent operations. guard_exec sanitizes command output, guard_read_file protects sensitive files, and guard_git_diff returns a masked diff. guard_apply_patch and guard_unmask_args restore values locally when writing files or dispatching parameters. RAG-specific tools sanitize chunks before embedding or storage and restore retrieved results for presentation.

Setup and configuration

Run the server directly with:

Terminal
npx -y @privacyscrubber/mcp-server

The automatic installer can configure detected clients:

Terminal
npx -y @privacyscrubber/mcp-server install --all

It also accepts targeted options such as --cursor, --claude, --claude-code, --vscode, or --zed. Smithery installation is available through:

Terminal
npx -y @smithery/cli install @privacyscrubber/mcp-server --write-to-clients

A Python-based launch path is available with uvx privacyscrubber-mcp or a PyPI installation. create_default_config creates privacyscrubber.json in the active workspace when that file does not exist; the generated structure includes areas for custom regular-expression rules and exclusion patterns.

Tools and capabilities

  • Inspect text without mutation using audit_context.
  • Sanitize or restore text with sanitize_text, scrub_text, and reveal_text.
  • Mask, scan, or redact files and directories with sanitize_file, scrub_file, audit_directory_for_pii, and redact_file.
  • Protect shell commands, files, Git diffs, patches, arguments, and agent instruction files through the Guard tools.
  • Process RAG chunks and retrieved results with guard_rag_chunk and guard_rag_restore.
  • Create compliance certificates, export audit logs, inspect session status, mark false positives, and reset sessions.
  • Check licensing and available profiles with check_status.

Limitations and notes

Token restoration depends on the local volatile-memory session map, so the relevant session must remain available when reveal_text, guard_apply_patch, or another restoration operation is used. mark_false_positive changes future sanitization behavior for the current session by excluding the selected plaintext value. redact_file modifies files in place and creates a .bak backup by default; dry_run=true can be used to test the operation without modifying the file.

For backend services, ETL pipelines, or non-MCP RAG integrations, the project points to the separate @privacyscrubber/sdk package rather than these MCP tools.

Read the full README View source on GitHub

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Frequently Asked Questions about Privacyscrubber MCP

moxno/privacyscrubber-mcp is an MCP server that connects AI clients to local privacy-scrubbing, auditing, file, shell, Git, and RAG protection workflows. Its main tools include sanitize_text, audit_context, reveal_text, guard_exec, guard_read_file, and guard_git_diff, which mask sensitive values before model processing and restore them locally when needed.

Install it by running `npx -y @privacyscrubber/mcp-server`, or use `npx -y @privacyscrubber/mcp-server install --all` to configure detected clients. Smithery, `uvx`, and PyPI installation paths are also available.

Yes, it supports Cursor. Run the installer with `--cursor`, use the automatic `--all` option, or configure the MCP server manually using the direct NPX command.

It restores masked values through the local volatile-memory session map. Pass the model's tokenized response to `reveal_text`, or use Guard restoration tools such as `guard_apply_patch`, `guard_unmask_args`, and `guard_rag_restore` for local operations.

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