Post

AI-Powered Network Inspection: A Journey into eBPF and the Model Context Protocol

Posted
How I built an MCP server that leverages eBPF to provide kernel-level network connection monitoring for AI tool.
How I built an MCP server that leverages eBPF to provide kernel-level network connection monitoring for AI tool.
By Simone Rodigari 7 min read

Introduction

In the ever-evolving landscape of network monitoring and observability, I embarked on an exciting project to explore two cutting-edge technologies: eBPF (Extended Berkeley Packet Filter) for kernel-level network monitoring and the Model Context Protocol (MCP) for AI-powered analysis. The result is a powerful duo of tools that demonstrate how modern system programming can be enhanced with artificial intelligence.

The Vision

The goal was ambitious yet focused: create a system that could monitor network connections at the kernel level using eBPF technology, then leverage AI to provide intelligent insights about network behavior patterns. This would serve as both a learning exercise in these emerging technologies and a practical tool for network analysis.

Project Architecture: A Tale of Two Servers

The solution consists of two complementary projects that work in harmony:

1. eBPF Server: The Data Engine

The ebpf-server project serves as the foundation—a high-performance HTTP API server that uses eBPF programs to monitor network connections directly in the Linux kernel.

Key Features:

  • Kernel-Level Monitoring: eBPF programs capture connect() syscalls with minimal overhead
  • RESTful API: Clean HTTP endpoints for easy integration
  • Protocol Intelligence: Automatic detection of TCP/UDP protocols and port-based service identification
  • Real-time Analytics: Live connection statistics and metrics
  • Low Overhead: Efficient kernel-space monitoring with negligible performance impact

Technical Highlights:

  • Written in Go with C eBPF programs
  • Supports both TCP connections and UDP connections that use connect()
  • Provides detailed connection metadata including timestamps, process information, and destination details
  • Includes comprehensive protocol detection and connection classification

2. NetSpy: The AI-Powered Client

The netspy project is a sophisticated MCP client that consumes data from the eBPF server and enhances it with AI-powered insights.

Key Features:

  • MCP Integration: Self-contained Model Context Protocol server with internal client communication
  • AI-Powered Analysis: OpenAI GPT-3.5-turbo integration for intelligent network behavior analysis
  • Interactive Interface: Command-line interface for real-time network exploration
  • Multiple Query Modes: Support for process names, PIDs, and time-based filtering
  • Pattern Recognition: Automated analysis of connection patterns and anomalies

The Learning Journey

Discovering eBPF

eBPF proved to be a fascinating technology that operates at the intersection of system programming and observability. Key learnings included:

  • Kernel Integration: eBPF programs run safely in kernel space, providing unprecedented visibility into system operations
  • Performance Benefits: The ability to filter and process data at the kernel level dramatically reduces overhead compared to userspace monitoring
  • Safety Guarantees: The eBPF verifier ensures programs are safe and cannot crash the kernel

Exploring the Model Context Protocol

The Model Context Protocol opened up new possibilities for AI integration:

  • Standardized AI Integration: MCP provides a clean, standardized way to expose tools and resources to AI systems
  • Tool Composition: The protocol enables building complex AI-powered workflows by combining simple tools
  • Interactive AI: Real-time conversation with AI about network data creates new possibilities for network analysis

Technical Deep Dive

eBPF Server Architecture

1
2
3
4
5

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   HTTP Client   │───▶│ HTTP API Server │───▶│  eBPF Programs  │
│ (curl, apps,    │    │     (REST)      │    │   (Kernel)      │
│  monitoring)    │    │                 │    │                 │
└─────────────────┘    └─────────────────┘    └─────────────────┘

The eBPF server captures network events through kernel hooks, processes them in userspace, and exposes them via HTTP endpoints:

  • POST /api/connection-summary - Get connection statistics for specific processes
  • GET/POST /api/list-connections - List detailed connection events with filtering
  • GET /health - Service health check

NetSpy MCP Architecture

1
2
3
4
5
6
7
8
9
10

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   netspy CLI    │───▶│   MCP Server    │───▶│  eBPF Server    │
│  (MCP Client)   │    │  (Internal)     │    │ (HTTP API)      │
└─────────────────┘    └─────────────────┘    └─────────────────┘
                              │
                              ▼
                       ┌─────────────────┐
                       │  OpenAI API     │
                       │ (GPT-3.5-turbo) │
                       └─────────────────┘

NetSpy implements a complete MCP server that provides four main tools:

  1. get_network_summary - Statistical overview of connection attempts
  2. list_connections - Detailed connection listings with filtering
  3. analyze_patterns - Automated pattern detection and analysis
  4. ai_insights - AI-powered interpretation of network behavior

Real-World Usage Examples

Interactive Network Analysis

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17

$ ./netspy
🔗 Network Telemetry MCP Server
Starting interactive mode...

netspy-mcp> summary --process curl
Process 'curl' made 5 outbound connection attempts over the last 60 seconds

netspy-mcp> analyze --process curl
Connection Analysis:
  Top destinations:
    172.217.164.78:443 (3 connections)
    8.8.8.8:53 (2 connections)
  Protocols: TCP (3), UDP (2)

netspy-mcp> insights "curl made 5 connections in 60 seconds"
🤖 AI Network Insights:
This connection pattern shows typical web client behavior...

Automated Monitoring

The system enables sophisticated monitoring workflows:

1
2
3
4
5
6
7
8

# Monitor nginx connection patterns
./netspy --tool analyze_patterns --process nginx

# Get AI insights about connection spikes
./netspy --tool ai_insights --summary-text "nginx made 250 connections in 60 seconds"

# List suspicious connection attempts
./netspy --tool list_connections --max-events 1000

Technical Challenges and Solutions

eBPF Complexity

Challenge: eBPF programming requires deep kernel knowledge and careful attention to safety constraints.

Solution: Started with simple connection tracking, gradually adding features like protocol detection and timestamp conversion. The Go eBPF ecosystem (particularly cilium/ebpf) provided excellent abstractions.

MCP Integration

Challenge: The Model Context Protocol was relatively new with limited examples for complex integrations.

Solution: Implemented a self-contained MCP server within the client application, allowing for rich tool composition while maintaining simplicity.

AI Context Management

Challenge: Providing meaningful network data context to AI models without overwhelming them.

Solution: Developed structured data formats and summary representations that give AI models the right level of detail for analysis.

Performance and Safety

eBPF Benefits

  • Minimal Overhead: Kernel-level filtering means only relevant events reach userspace
  • Safety: eBPF verifier ensures programs cannot crash the system
  • Efficiency: Direct access to kernel data structures eliminates context switching overhead

Production Considerations

  • Security: Root privileges required for eBPF operations
  • Monitoring: Comprehensive logging and health checks
  • Resource Management: Careful memory management in both kernel and userspace components

Future Directions

This project opens several exciting avenues for future exploration:

Enhanced eBPF Capabilities

  • Additional Protocols: Expand beyond TCP/UDP to capture ICMP, raw sockets
  • Performance Metrics: Add latency and throughput measurements
  • Security Events: Monitor for suspicious connection patterns

AI-Powered Features

  • Anomaly Detection: Use ML models to identify unusual network behavior
  • Predictive Analytics: Forecast network usage patterns
  • Automated Alerting: AI-driven notification system for network events

MCP Ecosystem Integration

  • Tool Composition: Connect with other MCP-enabled tools for comprehensive system analysis
  • Multi-Model Support: Integration with different AI models for specialized analysis
  • Real-time Streaming: Live data feeds for continuous AI-powered monitoring

Key Learnings

Technical Insights

  1. eBPF’s Power: The ability to safely run programs in kernel space is genuinely transformative for observability
  2. MCP’s Potential: The Model Context Protocol provides a clean abstraction for AI tool integration
  3. AI Integration Patterns: Combining structured system data with AI analysis creates new possibilities for system understanding

Development Lessons

  1. Start Simple: Both eBPF and MCP have steep learning curves—incremental development is key
  2. Safety First: Kernel programming requires extreme attention to safety and testing
  3. Tool Composition: Building modular tools that work together creates more value than monolithic solutions

Conclusion

This project successfully demonstrates the powerful combination of eBPF for high-performance system monitoring and the Model Context Protocol for AI-enhanced analysis. The result is a practical tool that showcases how modern system programming can be augmented with artificial intelligence.

The journey provided deep insights into kernel-level programming, network protocols, and AI integration patterns. More importantly, it opened up new possibilities for how we can understand and analyze system behavior using the combination of low-level monitoring and high-level AI reasoning.

Whether you’re interested in eBPF, MCP, or AI-powered system analysis, these projects provide a solid foundation for further exploration and development.

Getting Started

To explore these projects yourself:

  1. Clone the repositories:
    1
2

    git clone https://github.com/SRodi/ebpf-server.git
git clone https://github.com/SRodi/netspy.git
  2. Build the eBPF server:
    1
2
3

    cd ebpf-server
make build
sudo ./bin/ebpf-server
  3. Run the MCP client:
    1
2
3

    cd netspy
go build -o netspy ./cmd/netspy
./netspy
  4. Set up OpenAI integration (optional):
    1

    export OPENAI_API_KEY="your-api-key"

Start exploring the intersection of kernel-level monitoring and AI-powered analysis—the future of intelligent system observability awaits!

This project represents a learning journey into eBPF and MCP technologies. Both the ebpf-server and netspy repositories are available on GitHub under the MIT license.

References

mcp, ebpf
mcp ebpf linux go c llm ai
This post is licensed under CC BY 4.0 by the author.