Paiml-mcp-agent-toolkit: Zero-config AI Code Context Generator with Multi-language Support, Integrating AI Assistants via MCP

Paiml MCP Agent Toolkit

PMAT is a zero-configuration AI code context generation tool that provides functions such as code quality analysis, technical debt grading, mutation testing, repository health scoring, and semantic search. It supports over 17 programming languages and can be integrated with AI assistants such as Claude Code through the MCP protocol.

Developer tools Artificial intelligence chatbots #Code Analysis #AI Assistant #Quality Assessment #Multi-language .Rust

rating : 3 points

downloads : 5.1K

update time : 2025-12-29

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What is PMAT?

PMAT (Pragmatic Multi-language Agent Toolkit) is an intelligent code analysis toolkit designed specifically for AI assistants and developers. It can deeply analyze code repositories, generate AI-friendly context information, evaluate code quality, and provide practical improvement suggestions. PMAT follows the quality principles of the Toyota Production System to ensure that code quality is measurable and verifiable.

How to use PMAT?

PMAT offers multiple usage methods: you can directly analyze code through the command-line tool, integrate it into AI development tools such as Claude Code and Cline as an MCP server, or integrate it into the CI/CD pipeline to automatically perform quality checks. It is easy to install and can be used without complex configuration.

Applicable Scenarios

PMAT is particularly suitable for the following scenarios: 1) Provide code context for AI programming assistants. 2) Evaluate and monitor technical debt. 3) Verify the effectiveness of test suites. 4) Help new members quickly understand the code repository. 5) Conduct quality assessment before code refactoring. 6) Serve as a quality gate in the CI/CD pipeline.

Main Features

AI Context Generation

Automatically analyze the code repository and generate context documents suitable for AI assistants (such as Claude and GPT) to understand, supporting output in LLM-optimized format.

Technical Debt Grading

Use six orthogonal indicators to grade code quality from A+ to F, helping to identify and manage technical debt.

Mutation Testing

Verify the effectiveness of test suites to ensure that tests can detect errors in the code, supporting a mutation kill rate standard of over 85%.

Code Repository Scoring

Evidence-based assessment of code repository health, providing a quantitative score from 0 to 211, including a quick mode and a full analysis mode.

Semantic Search

Use natural language to search for functions, patterns, and implementations in the code repository without having to remember specific function names or file names.

MCP Integration

Provide 19 MCP tools that can be seamlessly integrated with AI development tools such as Claude Code and Cline to extend the capabilities of AI assistants.

Multi-language Support

Support over 17 programming languages, including mainstream languages such as Rust, TypeScript, Python, Go, Java, and C/C++.

Quality Gates

Provide pre-commit hooks and CI/CD integration to ensure that code quality meets standards and prevent quality degradation.

Advantages

Can be used out of the box without configuration.

Support over 17 programming languages, with a wide range of applications.

Deeply integrated with mainstream AI development tools.

Provide verifiable quality commitments and benchmark tests.

Follow scientific quality assessment methods.

Excellent performance, taking only 1.84 seconds to process 10K lines of code.

Limitations

Relatively high memory usage (about 287MB for 10K lines of code).

The analysis depth for some languages may be limited.

Requires a Rust environment for installation and operation.

May take more time for very large code repositories.

How to Use

Install PMAT

Install PMAT through the Cargo package manager, which is the most convenient installation method.

Generate AI Context

Generate a context document for the code repository for AI assistants to help them better understand the code.

Evaluate Technical Debt

Analyze code quality and give a grade from A+ to F to identify areas of code that need improvement.

Verify Test Quality

Verify the effectiveness of the test suite through mutation testing to ensure that tests can detect code problems.

Start the MCP Server

Start the MCP server to integrate with AI development tools and provide 19 analysis tools.

Usage Examples

Provide Code Context for New AI Assistants

When new members join the project or use a new AI programming assistant, they need to quickly understand the code repository structure and key implementations.

Quality Assessment Before Code Refactoring

Before large-scale code refactoring, it is necessary to evaluate the technical debt and risk areas of the current code.

Quality Gate in the CI/CD Pipeline

Automatically check code quality in the continuous integration process to prevent quality degradation.

Find Implementations of Specific Functions

During development, you need to find the code implementations for specific functions (such as error handling).

Frequently Asked Questions

Which programming languages does PMAT support?

How to install PMAT?

What makes PMAT different from other code analysis tools?

How is the Technical Debt Grading (TDG) calculated?

Will PMAT affect development performance?

How to integrate PMAT into my development tools?

Is PMAT free?

Related Resources

PMAT Complete Documentation

Complete documentation including installation guides, usage tutorials, API references, and best practices.

GitHub Code Repository

Source code and issue tracking for PMAT.

Rust API Documentation

Rust API reference documentation for PMAT.

MCP Tool Guide

Detailed description of the 19 MCP tools provided by PMAT.

PAIML Project Homepage

Homepage of the PAIML machine learning stack project to which PMAT belongs.

Benchmark Test Report

Performance benchmark tests and statistical analysis methods for PMAT.

🚀 Git Hooks for Claude Integration

These are quality enforcement hooks designed for the Claude Agent SDK integration, which help maintain high - quality code during the development process.

🚀 Quick Start

These hooks are designed to enforce quality standards during the commit process for the Claude Agent SDK integration.

📦 Installation

To install the pre - commit hook, you have two options:

Option 1: Copy the hook to your `.git/hooks` directory

# Copy the hook to your .git/hooks directory
cp .git - hooks/pre - commit - claude - integration.sh .git/hooks/pre - commit
chmod +x .git/hooks/pre - commit

Option 2: Use `git config` to set up the hooks directory

git config core.hooksPath .git - hooks

✨ Features

📋 Pre - Commit Hook

The pre - commit - claude - integration.sh hook enforces EXTREME TDD quality gates.

🔍 Quality Checks

SATD Detection: Zero tolerance for TODO/FIXME/HACK/XXX.
Rust Formatting: Enforces cargo fmt standards.
Clippy Lints: All warnings are treated as errors.
Complexity: Ensures code compiles (proxy for complexity).
TypeScript Build: Verifies bridge compilation.
Unit Tests: All tests must pass.
File Size: Warns about files >500 lines.

⚠️ Bypassing Hooks (Emergency Only)

If you need to bypass hooks in an emergency, you can use the following command:

git commit --no - verify

⚠️ Important Note

This should only be used in exceptional circumstances. Bypassing quality gates may introduce technical debt.

🛠️ Troubleshooting

Hook not running

# Check if hook is executable
ls -l .git/hooks/pre - commit

# Make it executable
chmod +x .git/hooks/pre - commit

TypeScript checks failing

# Install dependencies
cd bridge
npm install
npm run build

Rust checks failing

# Fix formatting
cd server
cargo fmt

# Fix clippy warnings
cargo clippy --fix

📚 Documentation

📊 Quality Metrics

The hooks enforce the following standards:

Property	Details
SATD Count	0 (Block commit)
Clippy Warnings	0 (Block commit)
Formatting	100% (Block commit)
Test Pass Rate	100% (Block commit)
File Size	500 lines (Warning only)