
paiml-mcp-agent-toolkit
Pragmatic AI Labs MCP Agent Toolkit - An MCP Server designed to make code with agents more deterministic
Stars: 79

PAIML MCP Agent Toolkit (PMAT) is a zero-configuration AI context generation system with extreme quality enforcement and Toyota Way standards. It allows users to analyze any codebase instantly through CLI, MCP, or HTTP interfaces. The toolkit provides features such as technical debt analysis, advanced monitoring, metrics aggregation, performance profiling, bottleneck detection, alert system, multi-format export, storage flexibility, and more. It also offers AI-powered intelligence for smart recommendations, polyglot analysis, repository showcase, and integration points. PMAT enforces quality standards like complexity โค20, zero SATD comments, test coverage >80%, no lint warnings, and synchronized documentation with commits. The toolkit follows Toyota Way development principles for iterative improvement, direct AST traversal, automated quality gates, and zero SATD policy.
README:
Zero-configuration AI context generation system with extreme quality enforcement and Toyota Way standards. Analyze any codebase instantly through CLI, MCP, or HTTP interfaces. Built by Pragmatic AI Labs.
๐ฏ v2.63.0 Release: Advanced Code Similarity Detection System! Industry-leading duplicate and similarity detection:
- ๐ 4 Clone Types: Exact (Type-1), Renamed (Type-2), Modified (Type-3), Semantic (Type-4) detection
- ๐ Entropy Analysis: Shannon entropy for complexity measurement and pattern detection
- ๐งฎ Advanced Algorithms: Winnowing, TF-IDF, Cosine Similarity, Jaccard Index, Levenshtein Distance
- ๐ Multi-Format Output: JSON, Markdown, CSV, SARIF, and Summary formats
- ๐ Performance: Optimized for 100K+ LOC with parallel processing
- ๐ง Full Integration: CLI commands, MCP tools, and comprehensive examples
๐ v2.39.0 Release: TDG System with MCP Integration & Advanced Monitoring! Production-ready technical debt analysis:
- ๐ Web Dashboard: Real-time monitoring with Axum-based interface and Server-Sent Events
- ๐ ๏ธ 6 MCP Tools: Enterprise-grade external integration (tdg_analyze_with_storage, tdg_system_diagnostics, etc.)
- ๐ Advanced Analytics: Metrics aggregation, performance profiling, bottleneck detection
- ๐จ Alert System: Configurable thresholds with multi-channel notifications
- ๐ค Multi-format Export: JSON, CSV, SARIF, HTML, Markdown, XML, Prometheus support
- ๐พ Storage Flexibility: Pluggable backends (Sled, RocksDB, InMemory) with trait abstraction
๐ง v2.14.0 Release: Technical Debt Elimination via TDD! Major fixes using Test-Driven Development:
- โ Language Detection Fixed: Functions now properly detected (was 0, now detects all)
- ๐ซ Zero Stub Implementations: All stub code eliminated with real implementations
- ๐ Complexity Reduced: Ruchy parser from 89 to โค4 cyclomatic complexity (95% reduction)
- ๐งช TDD Coverage: 80%+ test coverage on critical language detection paths
- ๐ญ Toyota Way Applied: ONE implementation principle, zero defect tolerance
๐ฏ v2.13.0: Technical Debt Grading (TDG) System! Complete code quality scoring with 6 orthogonal metrics:
- ๐ Comprehensive Scoring: Structural complexity, semantic complexity, code duplication, coupling analysis
- ๐ Documentation Coverage: Language-specific documentation pattern detection and scoring
- ๐จ Consistency Analysis: Naming conventions, indentation patterns, and code style consistency
- ๐ Grade Classification: A+ through F grading system with detailed component breakdowns
- ๐ Multi-Language Support: 10+ languages including Rust, Python, JavaScript, TypeScript, Go, Java, C/C++
- ๐ ๏ธ CLI & MCP Integration:
pmat analyze tdg
command and MCP tools for programmatic access- ๐ Project Analysis: Directory-level analysis with language distribution and aggregated scoring
๐ v2.10.0: Claude Code Agent Mode - "Always Working" Achievement! Transform PMAT into a persistent background quality agent:
- ๐ค Claude Code Integration: Native MCP server for seamless Claude Code integration
- ๐พ Persistent State: Monitoring state maintained across restarts with auto-save
- โ๏ธ Production Ready: Environment-specific configs for dev, prod, and CI/CD
- ๐ Real-time Monitoring: Continuous quality tracking with file system watching
- ๐๏ธ Service Architecture: Systemd deployment with health checks and auto-restart
๐ฏ v2.9.0: Universal Demo "Just Works" Achievement! Complete AI-powered repository intelligence with multi-language analysis:
- ๐ค AI-Powered Recommendations: Framework-aware repository recommendations with complexity-based learning tiers
- ๐ Multi-Language Intelligence: Advanced polyglot analysis with cross-language dependency detection
- ๐๏ธ Architecture Pattern Recognition: Microservices, Layered, Event-driven pattern detection with confidence scoring
- ๐ Repository Showcase Gallery: Curated collection of 8+ repositories across languages and complexity levels
- โก Universal Demo: Any GitHub repository URL โ Complete analysis with AI recommendations
- ๐ Enhanced Web Demo: Interactive visualizations with 3 new API endpoints (/api/recommendations, /api/polyglot, /api/showcase)
- Toyota Way Excellence: Zero compilation defects maintained throughout development
Choose your preferred installation method - PMAT is available across all major package ecosystems:
cargo install pmat
# macOS/Linux - Homebrew
brew install pmat
# Windows - Chocolatey
choco install pmat
# Ubuntu/Debian - APT
sudo apt install pmat # (via PPA - coming soon)
# Arch Linux - AUR
yay -S pmat
# Node.js - npm (global)
npm install -g pmat-agent
# Latest version
docker run --rm -v $(pwd):/workspace paiml/pmat:latest pmat --version
# Interactive analysis
docker run --rm -v $(pwd):/workspace -w /workspace paiml/pmat:latest pmat context
git clone https://github.com/paiml/paiml-mcp-agent-toolkit
cd paiml-mcp-agent-toolkit
make build
# Linux/macOS Quick Install
curl -sSfL https://raw.githubusercontent.com/paiml/paiml-mcp-agent-toolkit/master/scripts/install.sh | sh
# Windows PowerShell
# Download from: https://github.com/paiml/paiml-mcp-agent-toolkit/releases
# Analyze current directory
pmat context
# Technical Debt Grading (TDG) - v2.39.0!
pmat tdg . --include-components
# Start TDG web dashboard
pmat tdg dashboard --port 8081 --open
# TDG analysis with storage
pmat tdg server/src/tdg/analyzer_ast.rs --storage-backend sled
# Get complexity metrics
pmat analyze complexity --top-files 10
# Find technical debt
pmat analyze satd
# Code similarity detection - v2.63.0! ๐
pmat analyze duplicates --detection-type all # Find all types of duplicates
pmat analyze duplicates --format sarif # Export to SARIF format
pmat analyze duplicates --detection-type semantic --threshold 0.7
# Analysis with timeout control - NEW! ๐ง
pmat analyze complexity --timeout 30 # 30-second timeout
pmat analyze dead-code --timeout 60 # 60-second timeout
pmat analyze satd --timeout 45 # 45-second timeout
# Run quality gates
pmat quality-gate --strict
# Start MCP server
pmat mcp
# Analyze any GitHub repository with AI recommendations
cargo run --example analyze_github_repo -- --url https://github.com/rust-lang/rust-clippy
# Compare multiple repositories across languages
cargo run --example compare_repos
# Run quality gates on GitHub repositories
cargo run --example quality_gate_github -- https://github.com/owner/repo
# Start interactive web demo
pmat demo --serve
# Then visit http://localhost:8080 for:
# โข AI-powered repository recommendations
# โข Multi-language project intelligence
# โข Repository showcase gallery
# โข Interactive analysis visualizations
# Setup quality enforcement (one-time)
make setup-quality
# Start development with quality checks
make dev
# Create quality-enforced commit
make commit
# Verify sprint quality
make sprint-close
- Technical Debt Grading (TDG): 6-metric orthogonal code quality scoring with A+ through F grading
- Real-time Dashboard: Web-based monitoring with live metrics and performance tracking
- Advanced Analytics: Metrics aggregation, trend detection, bottleneck analysis
- Performance Profiling: Flame graph generation, CPU/I/O/Memory analysis
- Alert Management: Configurable thresholds with notification channels
- Multi-format Export: 8 export formats (JSON, CSV, SARIF, HTML, Markdown, XML, Prometheus)
- Storage Flexibility: Pluggable backends with tiered Hot/Warm/Cold architecture
- MCP Integration: 6 enterprise tools for external system integration
- Complexity Analysis: McCabe cyclomatic & cognitive complexity with AST precision
- Dead Code Detection: Graph-based reachability analysis across 30+ languages
- SATD Detection: Self-admitted technical debt with severity classification
- Documentation Coverage: Language-specific pattern detection with scoring algorithms
- Consistency Analysis: Naming conventions and code style consistency measurement
- Deep Context Generation: Multi-dimensional analysis optimized for AI agents
- Smart Recommendations: Framework-aware repository suggestions with complexity matching
- Polyglot Analysis: Cross-language dependency detection and architecture pattern recognition
- Repository Showcase: Curated gallery with learning pathways from beginner to expert
- Integration Points: Risk assessment of multi-language project coupling with mitigation strategies
- Quality Gates: Zero-tolerance enforcement (complexity โค20, SATD=0, coverage >80%)
- Quality Proxy: AI code interceptor with 7-stage validation pipeline
- PDMT Integration: Deterministic todo generation with embedded quality requirements
- Refactoring Engine: State machine-based code transformation with ACID snapshots
- MCP Protocol: 24 tools via unified pmcp SDK 1.3.0 server (includes 6 new TDG enterprise tools)
- TDG Web Dashboard: Axum-based real-time interface with SSE streaming
- HTTP API: RESTful with Server-Sent Events streaming
- CLI Interface: 50+ commands with POSIX-compliant exit semantics
- Complete Specification - Unified source of truth (36 sections)
- TDG Guide - NEW! Technical Debt Grading system documentation
- Transactional Hashed TDG - ๐ v2.38.0! Enterprise-grade TDG with caching, scheduling, and resource control
- API Reference - Service APIs and integration patterns
- CLI Reference - Complete command documentation
- Toyota Way Guide - Development workflow and standards
- Sprint Management - Task tracking and execution DAG
- Quality Gates - Enforcement mechanisms
- MCP Integration - Model Context Protocol setup
- PDMT Guide - Deterministic todo generation
- CI/CD Integration - Pipeline integration
PMAT implements Toyota Production System principles through rigorous static analysis:
- Kaizen (ๆนๅ): Iterative file-by-file improvement with measurable ฮQ metrics
- Genchi Genbutsu (็พๅฐ็พ็ฉ): Direct AST traversal, no heuristics
- Jidoka (่ชๅๅ): Automated quality gates with fail-fast semantics
- Zero SATD Policy: Compile-time enforcement of zero technical debt
// Unified service layer with dependency injection
pub trait Service: Send + Sync {
type Input: Serialize + DeserializeOwned;
type Output: Serialize + DeserializeOwned;
async fn process(&self, input: Self::Input) -> Result<Self::Output, Self::Error>;
}
// All protocols use unified request/response
#[derive(Serialize, Deserialize)]
pub struct UnifiedRequest {
pub operation: Operation,
pub params: Value,
pub context: RequestContext,
}
- Startup: 4ms hot, 127ms cold (mmap'd grammar cache)
- Analysis: 487K LOC/s single-thread, 3.9M LOC/s multi-core
- Memory: 47MB base + 312KB per KLOC
- SIMD: 43% vectorized paths, 2.7x AVX2 speedup
- Rust 1.80.0+
- Git (for repository analysis)
git clone https://github.com/paiml/paiml-mcp-agent-toolkit
cd paiml-mcp-agent-toolkit
# Setup Toyota Way quality enforcement
make setup-quality
# Build and test
make build
make validate
# Run examples
make examples
[dependencies]
pmat = "2.39.0"
use pmat::services::code_analysis::CodeAnalysisService;
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
let service = CodeAnalysisService::new();
// Generate AI-optimized context
let context = service.generate_context(".", None).await?;
// Analyze complexity with Toyota Way standards
let complexity = service.analyze_complexity(".", Some(10)).await?;
Ok(())
}
- Rust: Full cargo integration with syn AST
- TypeScript/JavaScript: SWC-based parsing
- Python: RustPython AST analysis
- C/C++: Tree-sitter with goto tracking
-
Ruchy: v1.5.0 support with advanced analysis
- Full AST parsing with 35+ token types
- Halstead metrics (volume, difficulty, effort, time, bugs)
- Dead code detection (unused functions/variables)
- Type inference for literals and binary operations
- Actor message flow analysis with deadlock detection
- Enhanced pattern matching complexity scoring
- Import/export dependency tracking
- Kotlin: Tree-sitter based analysis
- 30+ Languages: Via tree-sitter grammar support
PMAT provides 18 MCP tools via unified pmcp SDK server:
# Start MCP server (auto-detects transport)
pmat mcp
# Test with Claude Code
cargo run --example mcp_server_pmcp
cargo run --example test_pmcp_server
-
analyze_tdg
- Technical Debt Grading with 6-metric scoring -
analyze_tdg_compare
- Compare TDG scores between files/projects -
tdg_analyze_with_storage
- NEW v2.39.0! TDG analysis with configurable storage backends -
tdg_system_diagnostics
- NEW v2.39.0! Comprehensive system health monitoring -
tdg_storage_management
- NEW v2.39.0! Storage operations and management -
tdg_performance_profiling
- NEW v2.39.0! Performance analysis with flame graphs -
tdg_alert_management
- NEW v2.39.0! Alert configuration and monitoring -
tdg_export_data
- NEW v2.39.0! Multi-format data export (8 formats) -
analyze_complexity
- Complexity metrics -
analyze_satd
- Technical debt detection -
analyze_dead_code
- Unused code analysis -
quality_gate
- Comprehensive quality validation -
refactor_start
- Begin refactoring workflow -
pdmt_deterministic_todos
- Generate quality todos -
github_create_issue
- Create GitHub issues - AI recommendation tools for intelligent repository analysis
- And 10 more...
Transform PMAT into a persistent background quality agent that continuously monitors your codebase:
# Start agent as MCP server for Claude Code
pmat agent mcp-server
# Configure in Claude Code settings.json:
{
"mcpServers": {
"pmat": {
"command": "pmat",
"args": ["agent", "mcp-server"],
"env": {}
}
}
}
# Start monitoring a project
pmat agent start --project-path /path/to/project
# Check monitoring status
pmat agent status
# Stop monitoring
pmat agent stop
- Real-time Monitoring: File system watching with instant quality feedback
- Persistent State: Maintains metrics across restarts with auto-save
- Toyota Way Compliance: Enforces โค20 complexity with zero SATD tolerance
- Analysis Timeouts: Configurable timeouts prevent infinite hangs (NEW! ๐ง)
- Production Ready: Systemd service with health checks and auto-restart
- MCP Native: Seamless Claude Code integration via stdio transport
-
start_quality_monitoring
- Begin monitoring a project -
stop_quality_monitoring
- Stop monitoring -
get_quality_status
- Current quality metrics -
run_quality_gates
- Execute quality checks -
analyze_complexity
- Complexity analysis -
health_check
- Agent health status
See Claude Code Agent Guide for detailed setup and deployment instructions.
# Real-time TDG metrics
GET /api/metrics
# System health status
GET /api/health
# Storage statistics
GET /api/storage/stats
# Run TDG analysis
GET /api/analysis?path=src/main.rs
# System diagnostics
GET /api/diagnostics
# Real-time metrics stream (SSE)
GET /api/events
# Storage operations
POST /api/storage/operation
# AI-powered repository recommendations
GET /api/recommendations
# Multi-language project intelligence
GET /api/polyglot
# Repository showcase gallery
GET /api/showcase
# Core analysis APIs
GET /api/summary
GET /api/metrics
GET /api/hotspots
GET /api/dag
PMAT enforces extreme quality standards:
- Complexity: โค20 cyclomatic, โค15 cognitive
- Technical Debt: 0 SATD comments allowed
- Test Coverage: >80% with property-based testing
- Code Quality: 0 lint warnings, 0 dead code
- Documentation: Synchronized with every commit
# Run comprehensive quality analysis
pmat quality-gate --strict
# CI/CD integration
pmat analyze complexity --fail-on-violation
pmat analyze satd --fail-on-violation
pmat quality-gate --strict --fail-on-violation
PMAT follows Toyota Way development principles:
-
Setup quality enforcement:
make setup-quality
-
Start development:
make dev
- Make changes with documentation updates
-
Quality-enforced commit:
make commit
-
Sprint verification:
make sprint-close
All contributions must meet:
- Zero SATD comments
- Complexity โค20 per function
- Full test coverage
- Documentation updates
See CONTRIBUTING.md for detailed guidelines.
Licensed under the MIT License. See LICENSE for details.
Built with โค๏ธ by Pragmatic AI Labs
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Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

teams-ai
The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

ai-guide
This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

classifai
Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

chatbot-ui
Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

BricksLLM
BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

uAgents
uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

griptape
Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.