"Employee Attrition Analysis and Prediction MCP" matching MCP servers:

Enables AI coding agents to test C++ code by running static analysis, sanitizers (ASan, TSan, LSan, UBSan), and performance profiling through eight MCP tools.

MCP server for persistent project knowledge management, enabling AI assistants to store, search, and reuse project analysis insights with freshness checking and impact analysis.

An MCP server that provides six code review tools for use with Claude Code or any MCP client. Each tool reads a file from disk, pairs its contents with a structured review prompt, and returns the bundle for the LLM to evaluate.

A local MCP server that provides Sonar-grade static analysis (bugs, vulnerabilities, code smells) for TypeScript/JS and C# repositories, with CLI and dashboard support.

Enables static analysis of code snippets and public GitHub repositories using ruff and semgrep through a secure MCP SSE server.

An intelligent MCP toolset for software testers that monitors code changes, analyzes test impact, recommends tests, and assesses risk, supporting multiple AI coding frameworks via dual transport modes.

The server facilitates natural language interactions for exploring and understanding codebases, providing insights into data models and system architecture using a cost-effective, simple setup with support for existing Claude Pro subscriptions.

MCP server for TypeScript type resolution and dependency graph analysis using ts-morph and madge.

Transform your codebase into professional architectural decision records with intelligent AI analysis

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.

Provides comprehensive code analysis through three MCP servers: static analysis for code quality and security, dependency analysis for package management and vulnerabilities, and complexity analysis for maintainability assessment across multiple programming languages.

A modular MCP server for code analysis using ast-grep, enabling structural pattern matching and transformations across multiple languages.

An MCP server designed for SQL analysis that demonstrates sampling capabilities by interacting with SQL files in a local directory. It provides a framework for testing client-side sampling while managing SQL-based contexts.

Enables multi-project code analysis by invoking project-provided Claude Code agents and skills, with optional SVN updates, returning Markdown reports to MCP clients.

Local-first MCP server providing deterministic Flutter/Dart static analysis, knowledge search, and project review with structured insights and confidence-based findings over an indexed SQLite store of official repositories.

Analyzes frontend project code (React, Vue, Angular) and converts it into AI-understandable flow diagrams and object structures. Provides tools for code analysis, variable/function/component inspection, and project structure insights.

An MCP server that analyzes Python codebases using AST, stores code elements in a vector database, and enables natural language queries about code structure and functionality using RAG with Google's Gemini models.

A modular MCP server that provides tools for file operations, regex-based code searching, and structural analysis of functions and classes across multiple programming languages. It also includes AI-powered features for intelligently updating files according to architectural changes.

Enables AI agents to analyze and diff binaries by driving Ghidra headless or inside its GUI, exposing native reverse-engineering tools such as listing functions and segments, searching strings, decompiling, disassembling, and following cross-references. Ships with a devcontainer and supports local stdio or remote HTTP deployment.

Enables MCP clients to load eight on-demand coding skills and inspect project repositories through bounded, read-only map, search, read, diff, checks, and style tools. It keeps context lean by loading skill bodies only when needed and limiting repository scan and output sizes.