MCP Conceal

An MCP proxy that pseudo-anonymizes PII before data reaches external AI providers like Claude, ChatGPT, or Gemini.

sequenceDiagram
    participant C as AI Client (Claude)
    participant P as MCP Conceal
    participant S as Your MCP Server
    
    C->>P: Request
    P->>S: Request
    S->>P: Response with PII
    P->>P: PII Detection
    P->>P: Pseudo-Anonymization
    P->>P: Consistent Mapping
    P->>C: Sanitized Response

MCP Conceal performs pseudo-anonymization rather than redaction to preserve semantic meaning and data relationships required for AI analysis. Example: john.smith@acme.com becomes mike.wilson@techcorp.com, maintaining structure while protecting sensitive information.

Installation

Download Pre-built Binary

1. Visit the Releases page
2. Download the binary for your platform:

| Platform | Binary |
|----------|--------|
| Linux x64 | mcp-server-conceal-linux-amd64 |
| macOS Intel | mcp-server-conceal-macos-amd64 |
| macOS Apple Silicon | mcp-server-conceal-macos-aarch64 |
| Windows x64 | mcp-server-conceal-windows-amd64.exe |

3. Make executable: chmod +x mcp-server-conceal- (Linux/macOS)
4. Add to PATH:
- Linux/macOS: mv mcp-server-conceal- /usr/local/bin/mcp-server-conceal
- Windows: Move to a directory in your PATH or add current directory to PATH

Building from Source

git clone https://github.com/gbrigandi/mcp-server-conceal
cd mcp-server-conceal
cargo build --release

Binary location: target/release/mcp-server-conceal

Quick Start

Prerequisites

Install Ollama for LLM-based PII detection:

1. Install Ollama: ollama.ai
2. Pull model: ollama pull llama3.2:3b
3. Verify: curl http://localhost:11434/api/version

Basic Usage

Create a minimal mcp-server-conceal.toml:

[detection]
mode = "regex_llm"
[llm]

model = "llama3.2:3b"

endpoint = "http://localhost:11434"

See the Configuration section for all available options.

Run as proxy:

mcp-server-conceal \
  --target-command python3 \
  --target-args "my-mcp-server.py" \
  --config mcp-server-conceal.toml

Configuration

Complete configuration reference:

[detection]
mode = "regex_llm"                # Detection strategy: regex, llm, regex_llm
enabled = true                    
confidence_threshold = 0.8        # Detection confidence threshold (0.0-1.0)
[detection.patterns]

email = "\\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\.[A-Z|a-z]{2,}\\b"

phone = "\\b(?:\\+?1[-\\.\\s]?)?(?:\\(?[0-9]{3}\\)?[-\\.\\s]?)?[0-9]{3}[-\\.\\s]?[0-9]{4}\\b"

ssn = "\\b\\d{3}-\\d{2}-\\d{4}\\b"

credit_card = "\\b\\d{4}[-\\s]?\\d{4}[-\\s]?\\d{4}[-\\s]?\\d{4}\\b"

ip_address = "\\b(?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\\.){3}(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\\b"

url = "https?://[^\\s/$.?#].[^\\s]*"
[faker]

locale = "en_US"                  # Locale for generating realistic fake PII data

seed = 12345                      # Seed ensures consistent anonymization across restarts

consistency = true                # Same real PII always maps to same fake data
[mapping]

database_path = "mappings.db"     # SQLite database storing real-to-fake mappings

retention_days = 90               # Delete old mappings after N days
[llm]

model = "llama3.2:3b"             # Ollama model for PII detection

endpoint = "http://localhost:11434"

timeout_seconds = 180

prompt_template = "default"       # Template for PII detection prompts
[llm_cache]

enabled = true                    # Cache LLM detection results for performance

database_path = "llm_cache.db"

max_text_length = 2000

Configuration Guidance

Detection Settings:
- confidence_threshold: Lower values (0.6) catch more PII but increase false positives. Higher values (0.9) are more precise but may miss some PII.
- mode: Choose based on your latency vs accuracy requirements (see Detection Modes below)

Faker Settings:
- locale: Use "en_US" for American names/addresses, "en_GB" for British, etc. Affects realism of generated fake data
- seed: Keep consistent across deployments to ensure same real data maps to same fake data
- consistency: Always leave true to maintain data relationships

Mapping Settings:
- retention_days: Balance between data consistency and storage. Shorter periods (30 days) reduce storage but may cause inconsistent anonymization for recurring data
- database_path: Use absolute paths in production to avoid database location issues

Detection Modes

Choose the detection strategy based on your performance requirements and data complexity:

RegexLlm (Default)

Best for production environments - Combines speed and accuracy: - Phase 1: Fast regex catches common patterns (emails, phones, SSNs) - Phase 2: LLM analyzes remaining text for complex PII - Use when: You need comprehensive detection with reasonable performance - Performance: ~100-500ms per request depending on text size - Configure: mode = "regex_llm"

Regex Only

Best for high-volume, latency-sensitive applications: - Uses only pattern matching - no AI analysis - Use when: You have well-defined PII patterns and need <10ms response - Trade-off: May miss contextual PII like "my account number is ABC123" - Configure: mode = "regex"

LLM Only

Best for complex, unstructured data: - AI-powered detection catches nuanced PII patterns - Use when: Accuracy is more important than speed - Performance: ~200-1000ms per request - Configure: mode = "llm"

Advanced Usage

Claude Desktop Integration

Configure Claude Desktop to proxy MCP servers:

{
  "mcpServers": {
    "database": {
      "command": "mcp-server-conceal",
      "args": [
        "--target-command", "python3",
        "--target-args", "database-server.py --host localhost",
        "--config", "/path/to/mcp-server-conceal.toml"
      ],
      "env": {
        "DATABASE_URL": "postgresql://localhost/mydb"
      }
    }
  }
}

Custom LLM Prompts

Customize detection prompts for specific domains:

Template locations:
- Linux: ~/.local/share/mcp-server-conceal/prompts/
- macOS: ~/Library/Application Support/com.mcp-server-conceal.mcp-server-conceal/prompts/
- Windows: %LOCALAPPDATA%\\com\\mcp-server-conceal\\mcp-server-conceal\\data\\prompts\\

Usage:
1. Run MCP Conceal once to auto-generate default.md in the prompts directory:

   mcp-server-conceal --target-command echo --target-args "test" --config mcp-server-conceal.toml

   

2. Copy: cp default.md healthcare.md
3. Edit template for domain-specific PII patterns
4. Configure: prompt_template = "healthcare"

Environment Variables

Pass environment variables to target process:

mcp-server-conceal \
  --target-command node \
  --target-args "server.js" \
  --target-cwd "/path/to/server" \
  --target-env "DATABASE_URL=postgresql://localhost/mydb" \
  --target-env "API_KEY=secret123" \
  --config mcp-server-conceal.toml

Troubleshooting

Enable debug logging:

RUST_LOG=debug mcp-server-conceal \
  --target-command python3 \
  --target-args server.py \
  --config mcp-server-conceal.toml

Common Issues:
- Invalid regex patterns in configuration
- Ollama connectivity problems
- Database file permissions
- Missing prompt templates

Security

Mapping Database: Contains sensitive real-to-fake mappings. Secure with appropriate file permissions.

LLM Integration: Run Ollama on trusted infrastructure when using LLM-based detection modes.

Contributing

Contributions are welcome! Follow these steps to get started:

Development Setup

Prerequisites:
- Install Rust: https://rustup.rs/
- Minimum supported Rust version: 1.70+

1. Clone and setup:

   git clone https://github.com/gbrigandi/mcp-server-conceal

   cd mcp-server-conceal

   

2. Build in development mode:

   cargo build

   cargo test

   

3. Install development tools:

   rustup component add clippy rustfmt

   

4. Run with debug logging:

   RUST_LOG=debug cargo run -- --target-command cat --target-args test.txt --config mcp-server-conceal.toml

   

Testing

- Unit tests: cargo test
- Integration tests: cargo test --test integration_test
- Linting: cargo clippy
- Formatting: cargo fmt

Submitting Changes

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Make your changes and add tests
4. Ensure all tests pass: cargo test
5. Format code: cargo fmt
6. Submit a pull request with a clear description

License

MIT License - see LICENSE file for details.