MCP server providing automated code linting, rule explanations, and configuration templates for wemake-python-styleguide, with structured violation reports and offline rule database.
An MCP server that scans React and Vue projects, extracts component metadata (props, slots, events, imports, usage), and exposes it to AI coding agents via structured tools.
Exposes the 23 Gang of Four design patterns to AI coding agents for generation, canonical examples, AST-based detection, validation, and anti-pattern refactoring in Python codebases.
Enables deterministic static analysis of Python code, providing tools to inspect classes, functions, imports, dependencies, and more, without executing the code.
This MCP server provides direct access to ruff linting, formatting checks, and ty type-checking for Python projects, with token-efficient, structured output.
Provides AI assistants with access to private React component library documentation, props, and code examples through type-safe TypeScript integration.
A Model Context Protocol server that helps programmers understand code by providing explanations, tech stack analysis, and best practice suggestions through prompt templates.
A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.
A Model Context Protocol server for deep codebase understanding of Python projects, focusing on data analysis and scientific computing. It provides architectural analysis, pattern detection, dependency mapping, test coverage analysis, and AI-optimized context generation.
An enterprise-grade AI processing platform that provides advanced code analysis, automated documentation generation, security scanning, and knowledge extraction capabilities through a scalable MCP architecture. Features iterative neural processing, support for 100+ programming languages, and real-time streaming updates.
Integrates Google's Gemini AI models into Claude Code and other MCP clients to provide second opinions, code comparisons, and token counting. It supports streaming responses and multi-turn conversations directly within your existing AI development workflow.
Provides AI agents with ground-truth knowledge of Strapi projects by reading real schema files, enabling accurate queries and safe refactoring operations.
Enables Claude Code to leverage OpenAI models for expert code review, deep architecture analysis, and security audits, with automatic markdown report generation.
Enables Python code linting by integrating the Model Context Protocol with tools like pylint and the OpenAI API. It allows for dynamic tool discovery and uses LLMs to orchestrate tool selection and provide refined analysis of linting results.
Enables comprehensive security vulnerability scanning and code quality analysis for Python applications. Provides detailed reports with scoring, actionable suggestions, and comparison tracking specifically designed for backend developers working with frameworks like Django, Flask, and FastAPI.
Exposes the structure and documentation of the openreview-py library, enabling LLMs to discover classes, functions, and signatures for accurate code generation. It provides tools for searching the API and retrieving detailed metadata without executing code or making external API calls.