⚠️ 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 our Contributing Guidelines for exciting ways to participate without writing code!
✅ 18 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 via REPOSITORY_PATH
CodePrism is proudly sponsored by Dragonscale 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 (6 tools): Complexity analysis, data flow tracing, pattern detection, inheritance analysis
- Workflow Orchestration (4 tools): Batch processing, workflow suggestions, optimization guidance
- 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 │
│ (Claude/Cursor)│ JSON-RPC 2.0 │ Server │
└─────────────────┘ └──────────────────┘
│
┌────────────┴────────────┐
┌───────────────▼───────────────▼─────────────────┐
│ 18 MCP Tools │
│ ┌─────────────┐ ┌─────────────────────────┐ │
│ │ Core │ │ Search & Discovery │ │
│ │ Navigation │ │ 4 tools │ │
│ │ 4 tools │ └─────────────────────────┘ │
│ └─────────────┘ ┌─────────────────────────┐ │
│ ┌─────────────┐ │ Analysis │ │
│ │ Workflow │ │ 6 tools │ │
│ │ 4 tools │ │ │ │
8000
│ └─────────────┘ └─────────────────────────┘ │
└─────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────┐
│ Graph-Based Code Analysis │
│ JavaScript/TypeScript + Python Support │
└─────────────────────────────────────────────────┘
- Rust 1.82+ (for building from source)
- Any repository to analyze (JavaScript, Python, TypeScript, or mixed)
# Clone and build
git clone https://github.com/rustic-ai/codeprism
cd codeprism
cargo build --release
# Verify installation
./target/release/codeprism-mcp --help🏆 Claude Desktop - Best overall MCP experience
// ~/.config/claude-desktop/claude_desktop_config.json
{
"mcpServers": {
codeprism": {
"command": "/path/to/codeprism/target/release/codeprism-mcp",
"env": {
"REPOSITORY_PATH": "/path/to/your/repository"
}
}
}
}⚡ Cursor - AI pair programming with code intelligence
// .cursor/mcp.json
{
"mcpServers": {
codeprism": {
"command": "/path/to/codeprism/target/release/codeprism-mcp",
"env": {
"REPOSITORY_PATH": "."
}
}
}
}🔧 Manual Usage - Direct stdio communication
# Set repository path and run
export REPOSITORY_PATH=/path/to/your/repository
./target/release/codeprism-mcprepository_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 detection
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 by Dragonscale 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:
- 18/18 tools working (100% success rate)
- 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-mcpDownload Binary:
# Linux x86_64
wget https://github.com/rustic-ai/codeprism/releases/latest/download/codeprism-mcp-linux-x86_64
chmod +x codeprism-mcp-linux-x86_64
# macOS
wget https://github.com/rustic-ai/codeprism/releases/latest/download/codeprism-mcp-macos-x86_64
# Windows
# Download from: https://github.com/rustic-ai/codeprism/releases/latest/download/codeprism-mcp-windows-x86_64.exeDocker:
docker pull ghcr.io/rustic-ai/codeprism:latest
docker run -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 under MIT and Apache 2.0. See LICENSE-MIT and LICENSE-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