MCP Security Sanitizer

A small MCP server and interactive LangChain/Groq chatbot. The server exposes local regex-based tools for redacting common secrets and personally identifiable information (PII), and the client sanitizes each message before sending it to the language model.

Important limitation: Regex matching only recognizes the patterns implemented here. It cannot guarantee detection of every secret or PII value. The client guarantees that both configured sanitizer tools are called before each non-empty user message is passed to Groq; the system prompt is defense-in-depth, not the enforcement mechanism. Do not send regulated or highly sensitive data without adding appropriate controls and validating detection coverage for your environment.

Architecture

User input
   |
   v
Python client --MCP/SSE--> local regex scrubbers
   |                         1. mask_api_keys_and_secrets
   |                         2. mask_pii_data
   v
Sanitized text --> LangChain agent --> Groq

Sanitization uses local Python regular expressions in the MCP server and consumes 0 LLM tokens. The client makes the sanitizer calls synchronously before invoking the agent, so raw input is not sent to Groq by this chat flow. An MCP transport is still a network boundary; this configuration binds the server to loopback (127.0.0.1) and is intended for local development.

The logging interceptor prints tool names and argument field names but withholds all argument and result contents, so it does not log unsanitized input or potentially sensitive sanitizer output.

Tools

Requirements

• Python 3.11+
• uv
• A Groq API key

Setup

From the project directory, resolve dependencies, create the project-local .venv, and write or update uv.lock:

uv sync

You do not need to activate .venv when using uv run; it selects the project environment automatically.

Create a .env file in the project directory (beside pyproject.toml):

GROQ_API_KEY=your_groq_api_key
GROQ_MODEL=openai/gpt-oss-120b

Keep .env out of version control. The client loads this file at startup and checks that GROQ_API_KEY is set before connecting to Groq. A .env placed inside .venv is also recognized as a fallback, but the project directory is the recommended location because .venv is generated and can be deleted and recreated. GROQ_MODEL is optional; if omitted, the client uses openai/gpt-oss-120b. If Groq reports that a model is unavailable, set GROQ_MODEL to a model ID enabled for your account. The API key is used by ChatGroq; it is not sent to the MCP server.

Run

Start the MCP server in one terminal:

uv run server.py

It listens on http://127.0.0.1:8000 using the SSE transport, with the MCP endpoint at /sse.

Server startup screenshot

The image below illustrates the expected server terminal after startup. It is a generated example rather than a direct screenshot from your machine; process IDs and log formatting can differ by FastMCP/Uvicorn version.

In a second terminal, start the interactive chatbot:

uv run client.py

Alternatively, activate the project environment in each terminal and run Python directly. For example, in Windows PowerShell:

.\.venv\Scripts\Activate.ps1
python server.py

In a second activated terminal:

.\.venv\Scripts\Activate.ps1
python client.py

Type `e

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