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CloakLLM MCP Server

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CloakLLM MCP Server

MCP server that wraps CloakLLM's Python SDK as tools for Claude Desktop and other MCP-compatible clients.

Important: MCP does not protect your initial prompt. MCP tools are called by the LLM — your prompt is sent to the LLM provider first, then the LLM decides to invoke tools. This means the raw prompt (including any PII) reaches the provider before CloakLLM can sanitize it. The MCP server is useful for sanitizing data that the LLM processes during a conversation (documents, files, tool outputs), but it cannot prevent your prompt from reaching the provider.

To protect prompts before they leave your infrastructure, use the SDK middleware instead:

  • Python: enable_openai(client) or cloakllm.enable() (LiteLLM)

  • JavaScript: cloakllm.enable(client)

Tools

Tool

Description

sanitize

Detect & cloak PII, return sanitized text + token map ID + entity_details. Pass mode: "redact" for irreversible PII removal (no token_map_id returned).

desanitize

Restore original values using a token map ID

analyze

Detect PII without cloaking (pure analysis)

Related MCP server: Agent Guardrail MCP

Install

cd cloakllm-mcp
pip install -e .

Claude Desktop Configuration

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "cloakllm": {
      "command": "python",
      "args": ["/path/to/cloakllm-mcp/server.py"],
      "env": {
        "CLOAKLLM_LOG_DIR": "./cloakllm_audit",
        "CLOAKLLM_LLM_DETECTION": "false"
      }
    }
  }
}

Or using uvx:

{
  "mcpServers": {
    "cloakllm": {
      "command": "uvx",
      "args": ["mcp", "run", "/path/to/cloakllm-mcp/server.py"]
    }
  }
}

Usage Examples

Sanitize text before sending to an LLM

Tool call: sanitize

{
  "text": "Email john@acme.com about the meeting with Sarah Johnson at 742 Evergreen Terrace",
  "model": "claude-sonnet-4-20250514",
  "token_map_id": "optional-id-for-multi-turn"
}

Multi-turn: Pass the token_map_id from a previous sanitize response to reuse the same token map across conversation turns. The same PII will always map to the same token.

Response:

{
  "sanitized": "Email [EMAIL_0] about the meeting with [PERSON_0] at 742 Evergreen Terrace",
  "token_map_id": "a1b2c3d4-...",
  "entity_count": 2,
  "categories": {"EMAIL": 1, "PERSON": 1},
  "entity_details": [
    {"category": "EMAIL", "start": 6, "end": 19, "length": 13, "confidence": 0.95, "source": "regex", "token": "[EMAIL_0]"},
    {"category": "PERSON", "start": 42, "end": 56, "length": 14, "confidence": 0.85, "source": "spacy", "token": "[PERSON_0]"}
  ]
}

Restore original values

Tool call: desanitize

{
  "text": "I've drafted an email to [EMAIL_0] regarding [PERSON_0]'s request.",
  "token_map_id": "a1b2c3d4-..."
}

Response:

{
  "restored": "I've drafted an email to john@acme.com regarding Sarah Johnson's request."
}

Analyze text for PII (no cloaking)

Tool call: analyze

{
  "text": "Contact john@acme.com, SSN 123-45-6789"
}

Response:

{
  "entity_count": 2,
  "entities": [
    {"text": "john@acme.com", "category": "EMAIL", "start": 8, "end": 21, "confidence": 0.95, "source": "regex"},
    {"text": "123-45-6789", "category": "SSN", "start": 27, "end": 38, "confidence": 0.95, "source": "regex"}
  ]
}

Environment Variables

Variable

Default

Description

CLOAKLLM_LOG_DIR

./cloakllm_audit

Audit log directory

CLOAKLLM_AUDIT_ENABLED

true

Enable/disable audit logging

CLOAKLLM_SPACY_MODEL

en_core_web_sm

spaCy model for NER

CLOAKLLM_LLM_DETECTION

false

Enable LLM-based detection

CLOAKLLM_LLM_MODEL

llama3.2

Ollama model for LLM detection

CLOAKLLM_OLLAMA_URL

http://localhost:11434

Ollama endpoint

Testing

# Test with MCP inspector
python -m mcp dev server.py

# Or run directly
python server.py

See Also

License

MIT

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

ActivityStale
ResponsivenessNo issues

Resources

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