"Resources and Tools for Data Analysis" 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.

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.

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

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.

An MCP server that provides a persistent sandbox for AI coding agents to explore codebases server-side, returning only compact summaries to reduce context consumption.

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.

A Model Context Protocol server that helps programmers understand code by providing explanations, tech stack analysis, and best practice suggestions through prompt templates.

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

Provides documentation generation and analysis tools for AI agents, including JSDoc generation, README analysis, changelog generation, API documentation, and code comment 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.

Local MCP servers that give Claude Code access to other tools mid-session.

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.

Architecture-index MCP tools for kernelee apps that combine static analysis and runtime wiring into a JSON index, enabling coding agents to query control flow and dependencies via the Model Context Protocol.

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.

Code linting and style checking tools for AI agents, exposed as an MCP server. Supports style checks, naming conventions, complexity analysis, dead code detection, and import analysis.

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

MCP server providing configuration file tools for AI agents. Parse, validate, merge, convert, and diff configuration files across all common formats.