[Feature Request] MCP Server Integration for AI-Assisted Memory Forensics

MCP Server Integration Proposal

Background

As AI-assisted digital forensics becomes increasingly important, integrating Volatility3 with the Model Context Protocol (MCP) would enable AI agents to leverage Volatility3's powerful memory forensics capabilities directly.

Proposed Implementation

Add a native MCP Server module to Volatility3 that exposes core functionality to AI agents:

Core Tools:

1. Memory Image Management - Load and analyze memory dumps from various sources (raw, vmem, vmss, etc.)
2. Process Analysis - List and examine processes in memory
3. Module Analysis - Enumerate loaded kernel modules and drivers
4. Network Analysis - Investigate network connections and sockets
5. Registry Analysis - Extract and analyze Windows registry hives
6. Malware Detection - Scan for rootkits and suspicious activity
7. YARA Scanning - Apply YARA rules to memory images

Implementation Details:

• New module: olatility3/mcp/ with server.py, errors.py, main.py
• Uses astmcp library (same as angr's MCP implementation)
• Optional dependency: pip install volatility3[mcp]
• Entry point: python -m volatility3.mcp
• Supports stdio, SSE, and HTTP transports

Example Usage:
`python

AI agent can now:

1. Load a memory image

memory = await session.call_tool("load_memory_image", {"image_path": "./suspicious.dump"})

2. List processes

processes = await session.call_tool("list_processes", {"os_type": "windows"})

3. Scan for rootkits

findings = await session.call_tool("scan_for_rootkits", {"os_type": "windows"})

4. Apply YARA rules

matches = await session.call_tool("yara_scan", {"yara_rule": "rule test { condition: true }"})
`

Benefits

1. AI-Assisted Forensics - Enable AI agents to automate memory analysis and incident response
2. Malware Investigation - Streamline malware analysis workflows with AI-powered tool integration
3. Educational Value - Help newcomers learn memory forensics through AI-guided exploration
4. Industry Alignment - Follow the trend of MCP integration in security tools (angr already has MCP support)

Implementation Status

I've prepared a complete implementation with:

• 10 MCP tools covering memory image management, process analysis, module analysis, network analysis, registry analysis, malware detection, and YARA scanning
• Comprehensive test suite in ests/mcp/
• Full documentation in docs/mcp.md
• Optional dependency to avoid impacting core Volatility3 users

Questions for Maintainers

1. Is this feature aligned with Volatility3's roadmap?
2. Should the MCP server be a separate package or integrated into the main repository?
3. Any specific requirements or concerns about the implementation approach?

I'm happy to create a PR if this proposal is acceptable. The implementation is ready and tested.

Related Work

• angr has native MCP support: https://github.com/angr/angr/tree/master/angr/mcp
• MCP protocol: https://modelcontextprotocol.io/

Review the proposed volatility3/mcp/server.py, errors.py, and main.py entry points, along with tests/mcp/ and docs/mcp.md. First verify the proposed fastmcp dependency and transport requirements against the repository structure. Done means the scope is agreed with maintainers and the optional MCP integration, tests, documentation, and entry point are accepted as specified.