About GenPark Secure Secret Token Redactor
GenPark Secure Secret Token Redactor scans prompt logs with regex and entropy-based detection, then redacts API keys, JWT tokens, private keys, and credentials. It is for workflows that need to remove secrets from logs and can run as an MCP server or library.
Key features
- 01Regex-based secret scanning
- 02Entropy-based detection
- 03Redaction of API keys and JWTs
- 04Private key and credential redaction
- 05Python standard library only
Use cases
- 01Removing secrets from prompt logs
- 02Redacting credentials before log handling
MCP support
It runs as a native MCP server and exposes secret scanning and redaction to MCP clients. The README names Claude Desktop, Cursor, LobeChat, and agent DAGs as integration contexts.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEGenPark Secure Secret Token Redactor
High-speed regex and entropy-based scanner redacting API keys, JWT tokens, private keys, and credentials from prompt logs.
Part of the GenPark AI Agent Skills Ecosystem.
🌟 Key Features
- 100% Python Standard Library: Zero third-party runtime dependencies.
- MCP Native: Plugs directly into Claude Desktop, Cursor, LobeChat, and autonomous agent DAGs.
- Production Resilience: Strict schema conformance, zero data loss, enterprise hardened.
🚀 Installation & Usage
Run as an MCP server:
python mcp_server.py
Use directly as a library:
from client import *
See example_usage.py for comprehensive integration demonstrations.
📄 License
MIT License. Maintained by GenPark AI.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.