MemProcFS MCP Server

Overview

The MemProcFS MCP Server bridges the gap between AI assistants and live memory inspection of running Windows processes. By exposing a Model Context Protocol (MCP) interface, the server allows Claude or other MCP‑compatible clients to query, read, and manipulate process memory in real time. This capability is especially valuable for developers who need to debug, reverse engineer, or monitor applications directly from an AI workflow without leaving their natural language interface.

Problem Solved

Traditional memory debugging tools require specialized GUIs or command‑line utilities that are cumbersome to integrate into automated pipelines. Developers often struggle to combine code analysis, dynamic inspection, and AI reasoning in a single session. The MemProcFS MCP Server solves this by turning memory inspection into an API‑like service that AI assistants can call as if it were any other tool. This eliminates context switching and allows rapid iteration on hypotheses about program state.

Core Functionality

• Live Process Attachment: The server attaches to any target process by PID, exposing its memory map and loaded modules.
• Memory Read/Write: Clients can request raw byte reads or writes at arbitrary addresses, enabling dynamic patching or state inspection.
• Symbol and Module Resolution: The server can translate addresses into symbol names, providing human‑readable context for AI analysis.
• Thread and Handle Enumeration: It exposes thread lists, handle tables, and other low‑level structures that are essential for deep debugging or reverse engineering.

These features collectively provide a comprehensive view of an application's runtime state, which AI assistants can use to generate diagnostics, suggest fixes, or even automate patching.

Use Cases

• Dynamic Debugging: An AI assistant can ask the server to read a variable’s value, modify it, and observe the effect—all within a single conversational session.
• Reverse Engineering: By inspecting memory layouts and symbols, developers can map undocumented behavior while the AI generates explanations or documentation.
• Security Analysis: Security teams can monitor suspicious memory activity, detect injection attempts, and have the AI recommend mitigation steps.
• Automation Pipelines: Continuous integration systems can invoke the server to validate memory integrity before deployment.

Integration with AI Workflows

The MCP interface fits naturally into existing AI assistant pipelines. A client can send a tool request with the target process ID and desired operation, receive a structured response, and feed that back into the assistant’s reasoning loop. Because MCP is stateless and uses JSON over HTTP, it can be wrapped in any language or framework that supports standard web requests. This makes the MemProcFS MCP Server a drop‑in component for developers building AI‑augmented debugging or analysis tools.

Distinct Advantages

• Zero Configuration: Once the server is running, any MCP client can start querying processes without additional setup.
• Cross‑Language Compatibility: The server is written in Python, but its MCP surface is language‑agnostic, allowing use from JavaScript, Go, or Rust clients.
• High Performance: Leveraging the MemProcFS kernel driver ensures low‑latency memory access, which is critical for real‑time debugging scenarios.
• Extensibility: The server’s JSON schema can be extended with custom commands, enabling specialized operations such as automated patch generation or memory snapshotting.

In summary, the MemProcFS MCP Server empowers developers to harness AI assistants for sophisticated memory inspection and manipulation tasks, streamlining debugging workflows and opening new possibilities for automated analysis and reverse engineering.