mcpField
D
Defenter Proxy

Defenter Proxy

Local
github.com/Defenter-AISecurity3Updated 49w ago

About Defenter Proxy

Defenter Proxy is a semantic security proxy for AI coding agents, monitoring prompts, tool calls, and file and shell operations in the IDE. It applies policies that allow, redact, or block activity.

Key features

  1. 01Real-time analysis of sensitive data and risky behavior
  2. 02Intercepts prompts, MCP calls, and responses
  3. 03Monitors agent-triggered file and shell operations
  4. 04Allows, redacts, or blocks activity by policy
  5. 05Shows actions and decisions inside the IDE

Use cases

  1. 01Prevent coding agents from exposing secrets or customer data
  2. 02Review shell commands and file reads triggered by agents
  3. 03Track MCP tool calls in the IDE

MCP support

It intercepts and wraps MCP tool calls and responses, analyzes their content, and enforces security policies.

Configuration

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

mcp.json · Universal config
{
"mcpServers": {
"defenter-ai-defenter-proxy": {
"command": "uvx",
"args": [
"defenter-proxy"
]
}
}
}
Run commanduvx defenter-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
pypidefenter-proxy0.0.96

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

Markdown supported

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.