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:

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.