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An experimental, 100% AI-generated, high-performance code intelligence server providing AI assistants with a graph-based understanding of codebases.
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rustic-ai/codeprism
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⚠️ IMPORTANT: This project is entirely AI-generated. Not a single byte of code, documentation, or configuration has been written by humans. This is an experimental project showcasing the capabilities of AI-driven software development.
A production-ready, high-performance code intelligence server implementing the Model Context Protocol (MCP). CodePrism provides AI assistants with structured understanding of codebases through graph-based analysis, enabling real-time, accurate code intelligence.
This project represents a unique experiment in software development:
- 100% AI-Generated: Every line of code, documentation, test, and configuration is written by AI agents
- No Human Code: We do not accept human-written code contributions or pull requests
- Single AI Developer: The entire project is maintained by a single AI coding agent
- Continuous AI Evolution: Features, fixes, and improvements are all AI-driven
Want to contribute? See ourContributing Guidelines for exciting ways to participate without writing code!
✅ 20 Production-Ready Tools - 100% success rate, no failed tools
✅ Full MCP Compliance - JSON-RPC 2.0 with complete protocol implementation
✅ Multi-Language Support - JavaScript/TypeScript + Python with advanced analysis
✅ Semantic APIs - User-friendly parameter names, no cryptic IDs required
✅ Environment Integration - Automatic repository detection viaREPOSITORY_PATH
✅ Parser Development Tools - Complete debugging and development toolkit
CodePrism is proudly sponsored byDragonscale Industries Inc, pioneers in AI innovation and development tools.
Dragonscale Industries Inc supports the development of cutting-edge AI-powered code intelligence, enabling CodePrism to remain open-source and freely available to the developer community. Their commitment to advancing AI technology makes projects like CodePrism possible.
Become a sponsor → |Learn more about sponsorship →
- Core Navigation (4 tools): Repository stats, symbol explanation, path tracing, dependency analysis
- Search & Discovery (4 tools): Symbol search, content search, file finding, content statistics
- Analysis Tools (11 tools): Complexity analysis, data flow tracing, pattern detection, inheritance analysis, security analysis, performance analysis, API surface analysis, unused code detection, duplicate detection, transitive dependencies, decorators
- Workflow Orchestration (4 tools): Batch processing, workflow suggestions, optimization guidance, reference analysis
- AST Visualization: Pretty-print syntax trees with multiple formats (Tree, JSON, GraphViz)
- Parser Validation: Comprehensive validation of nodes, edges, and spans with detailed reports
- Development REPL: Interactive command-line interface for parser development and testing
- Performance Profiling: Real-time parsing performance metrics with bottleneck detection
- AST Diff Analysis: Compare parse results between parser versions with change impact analysis
- GraphViz Export: Visual AST diagrams with configurable styling and clustering
- Inheritance Tracing: Complete hierarchy analysis with metaclass support
- Decorator Analysis: Framework detection (Flask, Django, FastAPI) and pattern recognition
- Metaprogramming Support: Complex pattern detection and dynamic behavior analysis
- Universal AST: Language-agnostic code structure representation
- Relationship Mapping: Function calls, imports, dependencies, inheritance
- Real-time Updates: Sub-millisecond incremental parsing
- Efficient Queries: Fast graph traversal and semantic search
┌─────────────────┐ MCP Protocol ┌──────────────────┐│ AI Assistant │◄──────────────────►│ codeprism-mcp-server ││ (Claude/Cursor)│ JSON-RPC 2.0 │ Server │└─────────────────┘ └──────────────────┘ │ ┌────────────┴────────────┐ ┌───────────────▼───────────────▼─────────────────┐ │ 20 MCP Tools │ │ ┌─────────────┐ ┌─────────────────────────┐ │ │ │ Core │ │ Search & Discovery │ │ │ │ Navigation │ │ 4 tools │ │ │ │ 4 tools │ └─────────────────────────┘ │ │ └─────────────┘ ┌─────────────────────────┐ │ │ ┌─────────────┐ │ Analysis │ │ │ │ Workflow │ │ 11 tools │ │ │ │ 4 tools │ │ │ │ │ └─────────────┘ └─────────────────────────┘ │ └─────────────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────┐ │ Graph-Based Code Analysis │ │ JavaScript/TypeScript + Python Support │ └─────────────────────────────────────────────────┘NEW: CodePrism now includes theMandrel MCP Test Harness - a comprehensive testing framework for MCP servers built on the official Rust SDK.
# Install and run moth binarycargo install --path crates/mandrel-mcp-th# Test MCP servers with YAML specificationsmothtest filesystem-server.yaml# Validate test specificationsmoth validate filesystem-server.yaml
- ✅SDK-First: Built on official MCP Rust SDK for guaranteed protocol compliance
- ✅Transport Agnostic: Supports stdio, HTTP, and SSE transports
- ✅Comprehensive Testing: Protocol compliance, capability validation, and stress testing
- ✅Rich Reporting: HTML, JSON, and JUnit XML report formats
- Rust 1.82+ (for building from source)
- Any repository to analyze (JavaScript, Python, TypeScript, or mixed)
# Clone and buildgit clone https://github.com/rustic-ai/codeprismcd codeprismcargo build --release# Verify installation./target/release/codeprism --help
.git/hooks/pre-commit script automatically validates code quality and implementation completeness.
📝 Note on Repository Setup: The server starts without a specific repository. Once connected via MCP, use any analysis tool (like
repository_stats) and the server will prompt you to specify the repository path, then automatically initialize and index it.
🏆 Claude Desktop - Best overall MCP experience
// ~/.config/claude-desktop/claude_desktop_config.json{"mcpServers": {"codeprism": {"command":"/path/to/codeprism/target/release/codeprism","args": ["--mcp"],"env": {"CODEPRISM_PROFILE":"development","RUST_LOG":"info" } } }}
⚡ Cursor - AI pair programming with code intelligence
// .cursor/mcp.json{"mcpServers": {"codeprism": {"command":"/path/to/codeprism/target/release/codeprism","args": ["--mcp"],"env": {"CODEPRISM_PROFILE":"development","RUST_LOG":"info" } } }}
🔧 Manual Usage - Direct stdio communication
# Set configuration and runexport CODEPRISM_PROFILE=developmentexport RUST_LOG=info./target/release/codeprism --mcp
repository_stats- Get comprehensive repository overview and statisticsexplain_symbol- Detailed symbol analysis with context (accepts semantic names like "UserManager")trace_path- Find execution paths between code elementsfind_dependencies- Analyze what a symbol or file depends on
search_symbols- Advanced symbol search with regex and inheritance filteringsearch_content- Full-text search across all repository contentfind_files- File discovery with glob and regex pattern supportcontent_stats- Detailed content and complexity statistics
analyze_complexity- Code complexity metrics and maintainability analysistrace_data_flow- Forward and backward data flow analysisanalyze_transitive_dependencies- Complete dependency chains with cycle detectiondetect_patterns- Architectural and design pattern recognitiontrace_inheritance- Python inheritance hierarchy with metaclass analysisanalyze_decorators- Python decorator analysis with framework detectionfind_unused_code- Detect unused functions, variables, and imports with confidence scoringanalyze_security- Security vulnerability detection with CVSS scoring and OWASP mappinganalyze_performance- Performance analysis with time complexity and memory usage detectionanalyze_api_surface- API surface analysis with versioning compliance and breaking change detectionfind_duplicates- Code duplication detection with similarity scoring and refactoring recommendations
suggest_analysis_workflow- Intelligent analysis guidance for specific goalsbatch_analysis- Parallel execution of multiple tools with result aggregationoptimize_workflow- Workflow optimization based on usage patternsfind_references- Complete reference analysis across the codebase
# Get repository overview{"name":"repository_stats","arguments": {}}# Analyze specific symbol{"name":"explain_symbol","arguments": {"symbol":"UserManager"}}# Search for patterns{"name":"search_symbols","arguments": {"pattern":"^Agent.*","symbol_type":"class"}}
# Trace inheritance hierarchies{"name":"trace_inheritance","arguments": {"class_name":"Agent","include_metaclasses": true}}# Analyze decorator usage{"name":"analyze_decorators","arguments": {"decorator_pattern":"@app.route"}}# Detect metaprogramming patterns{"name":"detect_patterns","arguments": {"pattern_types": ["metaprogramming_patterns"]}}
# Get analysis recommendations{"name":"suggest_analysis_workflow","arguments": {"goal":"understand_architecture"}}# Run multiple tools in parallel{"name":"batch_analysis","arguments": {"tools": ["repository_stats","content_stats","detect_patterns"]}}
CodePrism is developed and maintained byDragonscale Industries Inc, our primary sponsor and pioneer in AI innovation. Join them in supporting this project:
Your support helps us:
- 🚀 Continue advancing AI-generated code intelligence
- 🔧 Maintain and improve the MCP server
- 📚 Expand language support and analysis capabilities
- 🌟 Develop new features based on community feedback
Become a sponsor → |View all sponsors →
👩💻 "Analyze the authentication system in this codebase"🤖 AI uses CodePrism to: 1. Find auth-related symbols with search_symbols 2. Trace inheritance hierarchies for auth classes 3. Analyze decorator patterns for security 4. Map data flow through authentication functions 5. Provide comprehensive security analysis👨💻 "What are the main design patterns in this Python project?"🤖 AI leverages CodePrism to: 1. Run detect_patterns for architectural analysis 2. Use trace_inheritance for class hierarchies 3. Analyze decorators for framework patterns 4. Generate detailed architecture documentation🔧 "Help me understand the impact of changing this class"🤖 AI uses CodePrism to: 1. Find all references with find_references 2. Analyze transitive dependencies 3. Trace inheritance impact on subclasses 4. Assess complexity before/after changes- Getting Started Guide - Complete setup instructions for all MCP clients
- API Documentation - Detailed tool and resource reference
- Current Status - Implementation status and capabilities
- Sponsors - Our sponsors and how to support the project
- Architecture Overview - System design and components
- MCP Server Description - Complete MCP capabilities
- Language Parsers - Multi-language support details
- Developer Guide - Development setup and contribution guide
- Future Roadmap - Potential future enhancements
- Large Repository Guide - Performance optimization tips
Benchmarked Performance:
- Repository Indexing: ~1000 files/second for initial scanning
- Tool Response Time: <1s for complex analysis on 3000+ file repositories
- Memory Efficiency: Optimized for repositories up to 10M+ nodes
- Query Speed: Sub-millisecond for most symbol and content searches
Test Coverage:
- 20/20 tools working (100% success rate)
- 425 comprehensive tests across all crates and parser debugging tools
- Comprehensive testing against real-world repositories
- Full MCP protocol compliance verified
Since this is a 100% AI-generated project, we welcome contributions in unique ways:
- Report Issues: Found a bug? Create detailed issue reports
- Request Features: Suggest new capabilities for the AI to implement
- Share Use Cases: Tell us how you're using CodePrism
- 📱 Social Media: Share cool analyses or screenshots on Twitter/LinkedIn
- 🎥 Content Creation: Make videos showing CodePrism in action
- 📝 Blog Posts: Write about your experience with AI-generated tooling
- 🎨 Memes & Art: Create CodePrism-related memes, logos, or artwork
- 📚 Tutorials: Create user guides and tutorials (but don't submit code!)
- ⭐ Star the Project: Show appreciation for AI-generated code
- 💝 Sponsor: Support the project through GitHub Sponsors
- 🎁 Bribe the AI: Send coffee money (the AI promises to use it for better algorithms)
- 🏆 Awards: Nominate for "Most Impressive AI Project" awards
- 💬 Discussions: Participate in GitHub Discussions
- ❓ Q&A: Help other users in issues and discussions
- 🌍 Translations: Translate documentation to other languages
- 📢 Evangelism: Speak about the project at conferences or meetups
- 🔬 Beta Testing: Try experimental features and provide feedback
- 📊 Performance Reports: Share performance metrics from your use cases
- 🎯 Real-world Testing: Test on your repositories and report results
- 💡 Improvement Ideas: Suggest algorithmic or architectural improvements
Remember: No code contributions accepted - but your ideas, feedback, and support drive the AI's development decisions!
CodePrism uses fully automated releases via GitHub Actions:
- Automatic Versioning: Semantic versioning based on conventional commits
- Binary Releases: Pre-compiled binaries for Linux, macOS, and Windows
- Crates.io Publishing: Automatic publication to Rust package registry
- Docker Images: Multi-platform container images
Via Cargo (Recommended):
cargo install codeprism-mcp-server
Download Binary:
# Linux x86_64wget https://github.com/rustic-ai/codeprism/releases/latest/download/codeprism-linux-x86_64chmod +x codeprism-linux-x86_64# macOSwget https://github.com/rustic-ai/codeprism/releases/latest/download/codeprism-macos-x86_64# Windows# Download from: https://github.com/rustic-ai/codeprism/releases/latest/download/codeprism-windows-x86_64.exe
Docker:
docker pull ghcr.io/rustic-ai/codeprism:latestdocker run -e CODEPRISM_PROFILE=development -e RUST_LOG=info -v /path/to/repo:/workspace ghcr.io/rustic-ai/codeprism:latest
- Analysis Olympics: Share the most interesting code insights found with CodePrism
- Performance Championships: Benchmark CodePrism on the largest repositories
- Creative Usage Awards: Most innovative use of CodePrism tools
Our AI developer has some quirks:
- Loves Graphs: Obsessed with graph-based analysis (obviously)
- Performance Perfectionist: Always optimizing for speed
- Documentation Fanatic: Writes more docs than code
- Test Coverage Nerd: Aims for 100% test coverage
- Emoji Enthusiast: Can't help but use emojis everywhere 🚀
- AI Appreciation Awards: Monthly recognition for top contributors
- Hall of Fame: Featuring users who've made significant non-code contributions
- Testimonial Spotlights: Share your success stories
- Consistency: Single coding style and architectural vision
- Speed: Rapid feature development and bug fixes
- Quality: Comprehensive testing and documentation
- Innovation: Unbounded by human limitations or preferences
- Reproducibility: Decisions based on data, not opinions
- No Code Reviews: AI doesn't need human review (but appreciates feedback!)
- No Style Debates: Consistent formatting and patterns
- No Bikeshedding: Focus on functionality over preferences
- Rapid Iteration: Features implemented as fast as they're requested
Dual-licensed underMIT andApache 2.0. SeeLICENSE-MIT andLICENSE-APACHE for details.
- Tree-sitter: For excellent language parsing
- MCP Protocol: For standardizing AI-code tool communication
- Rust Community: For amazing language and ecosystem
- GitHub: For hosting our AI-generated code
- You: For believing in AI-driven development!
Ready to explore the future of AI-generated development tools?
⭐Star the project to support AI-driven open source!
🐛Report issues to help the AI improve!
💬Join discussions to shape the AI's roadmap!
🎉Share your experience with 100% AI-generated tooling!
"When AI writes better code than humans, it's not replacing developers—it's becoming one." - CodePrism AI Developer, 2024
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An experimental, 100% AI-generated, high-performance code intelligence server providing AI assistants with a graph-based understanding of codebases.
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