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MCPower Security Proxy

Local
github.com/MCPower-SecuritySecurity3Updated 49w ago

About MCPower Security Proxy

MCPower Security Proxy is a Python proxy and local middleware that adds real-time policy checks to AI coding-agent activity. It is intended for developers using IDE-based agents and MCP tools, with decisions based on action content and intent.

Key features

  1. 01Intercepts MCP calls and responses
  2. 02Monitors prompts, file reads, and shell actions
  3. 03Detects sensitive data and risky behavior
  4. 04Allows, redacts, or blocks activity
  5. 05Shows live decisions in the IDE

Use cases

  1. 01Prevent secrets or personal data from leaving agent workflows
  2. 02Review agent and MCP activity within an IDE

MCP support

It wraps MCP server tool calls and responses, inspecting them and applying allow, redact, or block decisions.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"mcpower-security-mcpower-proxy": {
"command": "uvx",
"args": [
"mcpower-proxy"
]
}
}
}
Run commanduvx mcpower-proxy

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
pypimcpower-proxy0.0.91

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Defenter

Real-time semantic security for AI coding agents and MCP tools in VS Code, Claude Code and Cursor.

Defenter monitors every prompt, every coding agent tool call, every MCP server call, and key file and shell operations in your IDE. It acts as a semantic policy broker that understands what agents are doing, not just where they send data, so you can use AI coding agents without leaking secrets or customer data.


🚀 How to use

The simplest way to use Defenter is to install the VS Code or Cursor extension:

Once installed, the extension automatically intercepts and protects:

  • MCP server calls
  • Coding agent prompts and responses
  • File reads and shell commands triggered by the agent

No manual MCP configuration is needed.


Overview

Defenter is a semantic policy broker for AI coding agents. It adds an intelligent security layer inside your IDE that:

  • Intercepts every coding agent prompt and action
  • Wraps every MCP tool call and response
  • Analyzes the payload for sensitive information and risky behavior in real time
  • Enforces your security policies with allow, redact, or block decisions

Traditional security tools cannot see what an agent is about to share or execute. They look at apps and destinations, not at the intent and content of an agent’s actions.

Defenter bridges this gap by:

  • Preventing data leaks and context contamination
  • Providing clear, visual monitoring of every agent decision

Architecture and how it works

This repository contains the Defenter proxy and related components that secure MCP and coding agent traffic.

Defenter is built as a Python based proxy and local middleware that the IDE extension uses to enforce policy. At a high level:

  • Local middleware layer

    • Runs on the developer machine
    • Hooks coding agent prompts, file reads, and shell executions
    • Intercepts all MCP tool calls and responses
    • Performs client side redaction of secrets and PII
  • Cloud powered policy engine

    • Receives a minimal, redacted payload
    • Uses a classifier and analyzer to check for data leaks, context contamination, and prompt injection
    • Returns Allow, Redact, Need more info, or Block decisions in real time
    • Works with low latency to make sure the development flow in without friction
  • IDE integration

    • Seamless integration with VS Code and Cursor extensions
    • Shows a live monitoring view of all agent actions and Defenter decisions directly inside the IDE

Together, these pieces let you harness AI coding agents and MCP tools without compromising the security of your code, data, or workflows.


References

Python Proxy: See src/README.md for detailed implementation documentation

VSC Extension: See targets/vsc-extension/README.md for installation and user guide

Comments

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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.