PAIML MCP Agent Toolkit
Zero-configuration AI context generation system that analyzes any codebase instantly through CLI, MCP, or HTTP interfaces. Built by Pragmatic AI Labs.
๐ Installation
curl -sSfL https://raw.githubusercontent.com/paiml/paiml-mcp-agent-toolkit/master/scripts/install.sh | sh
๐ Tool Usage
CLI Interface
# Zero-configuration context generation
pmat context # Auto-detects language
pmat context --format json # JSON output
pmat context rust # Force language
# Code analysis
pmat analyze complexity --top-files 5 # Complexity analysis
pmat analyze churn --days 30 # Git history analysis
pmat analyze dag --target-nodes 25 # Dependency graph
pmat analyze dead-code --format json # Dead code detection
pmat analyze satd --top-files 10 # Technical debt
pmat analyze deep-context --format json # Comprehensive analysis
pmat analyze big-o # Big-O complexity analysis
pmat analyze makefile-lint # Makefile quality linting
pmat analyze proof-annotations # Provability analysis
pmat analyze graph-metrics # Graph centrality metrics
pmat analyze name-similarity "function_name" # Semantic name search
# Project scaffolding
pmat scaffold rust --templates makefile,readme,gitignore
pmat list # Available templates
# Refactoring engine
pmat refactor interactive # Interactive refactoring
pmat refactor serve --config refactor.json # Batch refactoring
pmat refactor status # Check refactor progress
pmat refactor resume # Resume from checkpoint
# Demo and visualization
pmat demo --format table # CLI demo
pmat demo --web --port 8080 # Web interface
pmat demo --repo https://github.com/user/repo # Analyze GitHub repo
๐ซ See CLI usage in action Context and code analysis: Running demos/visualization:
MCP Integration (Claude Code)
# Add to Claude Code
claude mcp add paiml-toolkit ~/.local/bin/pmat
๐ซ See Claude Code usage in action
Available MCP tools:
generate_template
- Generate project files from templatesscaffold_project
- Generate complete project structureanalyze_complexity
- Code complexity metricsanalyze_code_churn
- Git history analysisanalyze_dag
- Dependency graph generationanalyze_dead_code
- Dead code detectionanalyze_deep_context
- Comprehensive analysisgenerate_context
- Zero-config context generationanalyze_big_o
- Big-O complexity analysis with confidence scoresanalyze_makefile_lint
- Lint Makefiles with 50+ quality rulesanalyze_proof_annotations
- Lightweight formal verificationanalyze_graph_metrics
- Graph centrality and PageRank analysisrefactor_interactive
- Interactive refactoring with explanations
HTTP API
# Start server
pmat serve --port 8080 --cors
# API endpoints
curl "http://localhost:8080/health"
curl "http://localhost:8080/api/v1/analyze/complexity?top_files=5"
curl "http://localhost:8080/api/v1/templates"
# POST analysis
curl -X POST "http://localhost:8080/api/v1/analyze/deep-context" \
-H "Content-Type: application/json" \
-d '{"project_path":"./","include":["ast","complexity","churn"]}'
๐ง Supported Languages
- Rust - Complete AST analysis, complexity metrics
- TypeScript/JavaScript - Full parsing and analysis
- Python - AST analysis and code metrics
- C/C++ - Goto tracking, macro analysis, memory safety indicators
- Cython - Hybrid Python/C analysis
๐ Documentation
Feature Documentation
- Feature Overview - Complete feature index
- Makefile Linter - 50+ rules for Makefile quality
- Emit-Refactor Engine - Real-time defect detection & refactoring
- Excellence Tracker - Code quality metrics tracking
- Technical Debt Gradient - Quantitative debt measurement
- MCP Protocol - AI agent integration guide
Additional Features
Code Quality Tools
pmat analyze makefile-lint
- Lint Makefiles with 50+ quality rulespmat excellence-tracker
- Track code quality metrics over timepmat refactor serve
- Batch refactoring with checkpointspmat refactor interactive
- Interactive refactoring with explanations
Advanced Analysis
pmat analyze tdg
- Calculate Technical Debt Gradientpmat analyze proof-annotations
- Lightweight formal verificationpmat analyze defect-prediction
- ML-based defect predictionpmat analyze name-similarity
- Semantic name search with embeddingspmat analyze big-o
- Big-O complexity with confidence scorespmat analyze graph-metrics
- PageRank and centrality metricspmat analyze incremental-coverage
- Coverage changes since base branch
๐ Output Formats
- JSON - Structured data for tools and APIs
- Markdown - Human-readable reports
- SARIF - Static analysis format for IDEs
- Mermaid - Dependency graphs and diagrams
๐ฏ Use Cases
For AI Agents
- Context Generation: Give AI perfect project understanding
- Code Analysis: Deterministic metrics and facts
- Template Generation: Scaffolding with best practices
For Developers
- Code Reviews: Automated complexity and quality analysis
- Technical Debt: SATD detection and prioritization
- Onboarding: Quick project understanding
- CI/CD: Integrate quality gates and analysis
For Teams
- Documentation: Auto-generated project overviews
- Quality Gates: Automated quality scoring
- Dependency Analysis: Visual dependency graphs
- Project Health: Comprehensive health metrics
๐ Documentation
- CLI Reference
- MCP Protocol
- HTTP API
- Architecture
- Distributed Testing
๐งช Testing
The project uses a distributed test architecture for fast feedback:
# Run specific test suites
make test-unit # <10s - Core logic tests
make test-services # <30s - Service integration
make test-protocols # <45s - Protocol validation
make test-e2e # <120s - Full system tests
make test-performance # Performance regression
# Run all tests in parallel
make test-all
# Coverage analysis
make coverage-stratified
๐ค Contributing
- Fork the repository
- Create a feature branch
- Run
make test-fast
for validation - Submit a pull request
๐ License
MIT License - see LICENSE file for details.
Built with โค๏ธ by Pragmatic AI Labs