bpftrace MCP Server

# MCPtrace vs GPTtrace: Comprehensive Comparison and Improvement Opportunities ## Executive Summary MCPtrace and GPTtrace are two related projects for Linux kernel tracing, but with fundamentally different architectures and approaches. GPTtrace is an experimental CLI tool that uses OpenAI's GPT models to generate eBPF programs from natural language. MCPtrace is a more recent project that implements a Model Context Protocol (MCP) server for bpftrace, providing a standardized interface for AI assistants to interact with kernel tracing capabilities. ## Project Overview ### MCPtrace - **Architecture**: MCP server implementation - **Languages**: Rust (main), Python (legacy) - **Framework**: rmcp crate for MCP protocol - **Integration**: Claude Desktop/Code via MCP - **Execution Model**: Async with buffered output - **Status**: Active development ### GPTtrace - **Architecture**: CLI tool with direct OpenAI integration - **Language**: Python - **Framework**: LangChain, OpenAI SDK - **Integration**: Direct CLI usage - **Execution Model**: Synchronous with retries - **Status**: Experimental (recommends MCPtrace for production) ## Feature Comparison ### 1. AI Integration Approach **GPTtrace**: - Direct OpenAI API integration - Function calling for bpftrace parameter structuring - Vector database (FAISS) for example retrieval - Supports multiple LLM providers via LiteLLM - Retry mechanism with error feedback (up to 5 retries) **MCPtrace**: - Protocol-based integration via MCP - No direct LLM integration (handled by MCP client) - Simple example storage in JSON - LLM-agnostic design - No built-in retry mechanism ### 2. Execution Capabilities **GPTtrace**: ```python # Features: - Generate eBPF programs from natural language - Execute pre-built bcc tools - Save generated programs to files - Explain execution results - Interactive prompt for confirmation ``` **MCPtrace**: ```rust // Features: - List available probes with filtering - Get system BPF information - Execute bpftrace programs asynchronously - Retrieve execution results with pagination - Real-time output buffering ``` ### 3. Security and Authentication **GPTtrace**: - Interactive sudo prompt - User confirmation before execution - No persistent credential storage **MCPtrace**: - Environment variable for sudo password - Support for passwordless sudo configuration - Session-based credential caching (Python version) ### 4. Technical Implementation **GPTtrace Architecture**: ``` User Input → GPT API → bpftrace Function Call → Execution → Result Explanation ↓ Vector DB Search for Examples ``` **MCPtrace Architecture**: ``` MCP Client → MCP Server → bpftrace Execution → Buffered Output ↓ Async Task Management ``` ## Key Strengths and Weaknesses ### GPTtrace Strengths 1. **AI-Native Design**: Built specifically for LLM interaction 2. **Example Learning**: Vector database for finding relevant examples 3. **Error Recovery**: Automatic retry with error feedback 4. **Result Explanation**: AI explains the tracing results 5. **Multiple LLM Support**: Can use different AI providers 6. **Interactive Safety**: Requires user confirmation ### GPTtrace Weaknesses 1. **Synchronous Execution**: Blocks during program execution 2. **Limited Scalability**: Single-request model 3. **No Standardized Protocol**: Custom implementation 4. **Resource Management**: No cleanup or buffer limits ### MCPtrace Strengths 1. **Standard Protocol**: MCP compliance for broad compatibility 2. **Async Architecture**: Non-blocking execution model 3. **Resource Management**: Buffer limits and cleanup 4. **Performance**: Rust implementation for efficiency 5. **Tool Integration**: Native Claude integration ### MCPtrace Weaknesses 1. **No AI Features**: No built-in program generation 2. **Limited Error Recovery**: No automatic retries 3. **No Example Learning**: Static example set 4. **No Result Interpretation**: Raw output only ## Improvement Opportunities for MCPtrace Based on GPTtrace's features, here are key improvements MCPtrace could implement: ### 1. Enhanced Example Management ```rust // Add vector database support for example retrieval struct ExampleStore { embeddings: EmbeddingModel, examples: VectorDB, } impl ExampleStore { async fn find_similar(&self, query: &str, limit: usize) -> Vec<Example> { // Implement semantic search } } ``` ### 2. Intelligent Error Handling ```rust // Add retry mechanism with error analysis struct RetryConfig { max_attempts: u32, analyze_errors: bool, suggest_fixes: bool, } async fn exec_with_retry( program: &str, config: RetryConfig ) -> Result<ExecutionResult> { // Implement retry logic with error analysis } ``` ### 3. Program Generation Hints ```rust // Add tool for suggesting eBPF programs #[tool(description = "Suggest eBPF programs based on user intent")] async fn suggest_program( Parameters(SuggestRequest { intent, examples }): Parameters<SuggestRequest> ) -> Result<CallToolResult> { // Return suggested programs based on intent and examples } ``` ### 4. Result Analysis Tool ```rust // Add tool for analyzing trace results #[tool(description = "Analyze and summarize trace results")] async fn analyze_results( Parameters(AnalyzeRequest { execution_id, query }): Parameters<AnalyzeRequest> ) -> Result<CallToolResult> { // Provide structured analysis of results } ``` ### 5. Interactive Safety Features ```rust // Add confirmation mechanism struct SafetyConfig { require_confirmation: bool, dry_run: bool, sandbox_mode: bool, } ``` ### 6. Enhanced Probe Discovery ```rust // Add intelligent probe suggestions #[tool(description = "Suggest relevant probes based on tracing intent")] async fn suggest_probes( Parameters(ProbeRequest { intent }): Parameters<ProbeRequest> ) -> Result<CallToolResult> { // Analyze intent and suggest relevant probes } ``` ### 7. Example-Based Learning ```toml # Add to Cargo.toml [dependencies] qdrant-client = "1.0" # Vector database candle = "0.3" # Embeddings ``` ### 8. Program Validation ```rust // Add syntax validation before execution async fn validate_program(program: &str) -> Result<ValidationResult> { // Check syntax, probe availability, safety } ``` ### 9. Execution Templates ```rust // Add common tracing templates struct Template { name: String, description: String, program: String, parameters: Vec<Parameter>, } impl Template { fn instantiate(&self, params: HashMap<String, String>) -> String { // Generate program from template } } ``` ### 10. Result Streaming ```rust // Add SSE transport for real-time streaming async fn stream_results( execution_id: String, tx: Sender<Event> ) -> Result<()> { // Stream results as they arrive } ``` ## Implementation Roadmap ### Phase 1: Core Enhancements (Week 1-2) 1. Add program validation tool 2. Implement retry mechanism 3. Add example suggestion tool 4. Create result analysis tool ### Phase 2: AI Features (Week 3-4) 1. Add vector database for examples 2. Implement semantic search 3. Add program generation hints 4. Create template system ### Phase 3: Safety and UX (Month 2) 1. Add interactive confirmation 2. Implement dry-run mode 3. Add sandbox execution 4. Create execution history ### Phase 4: Advanced Features (Month 3) 1. Real-time streaming support 2. Multi-execution management 3. Resource quotas 4. Performance profiling ## Conclusion While MCPtrace provides a solid foundation with its MCP-compliant architecture, it could significantly benefit from GPTtrace's AI-native features. The key improvements focus on: 1. **Intelligence**: Adding semantic understanding and example-based learning 2. **Safety**: Interactive confirmation and validation 3. **Usability**: Templates, suggestions, and result analysis 4. **Reliability**: Retry mechanisms and error recovery By incorporating these features while maintaining its architectural advantages, MCPtrace could become the definitive solution for AI-assisted kernel tracing.