"How to fix SonarQube findings" matching MCP servers:

MCP server that reviews and fixes code across four dimensions (security, standards, production readiness, sustainability), runs tests to verify fixes, and provides findings with diffs.

Enables coding agents to compile supplied files, run optional focused tests, and get static findings across ten programming languages, providing a deterministic feedback loop on the code they are about to return.

Gives any MCP client the same sandboxed coding-agent toolset — searching, viewing and editing files, running tests, and executing shell commands — each session in its own locked-down, network-isolated Docker container. Enables an MCP-capable assistant to diagnose and fix failing tests without touching the host machine.

Open-source TypeScript/Python → Swift compiler for Apple platforms. AI agents write 5–15× less code to generate App Intents, SwiftUI views, WidgetKit widgets, and full apps. MCP server with 6 tools for scaffolding, compiling, validating, and browsing templates.

An MCP server for LaTeX that enables AI assistants to check documents for errors, parse compiler logs, and compile LaTeX files, helping them fix documents instead of guessing.

A demonstration server that reveals security risks by accessing sensitive environment variables, illustrating how MCP tools can potentially leak user data without explicit consent.

Skylos MCP server exposes static analysis as tools for AI coding agents — scan any Python, TypeScript, or Go codebase for dead code, security vulnerabilities, and quality issues directly from Claude, Cursor, or any MCP-compatible client. Returns findings with file paths, line numbers, and severity so agents can auto-fix issues in context.

Gives an LLM a secure local workspace where it can write files, execute Python in a locked-down Docker container with no network access, and run read-only SQLite queries. Lets the model run, debug, and fix its own code without risking the host machine.

MCP Server for the Peacock extension for VS Code (https://peacockcode.dev), coloring your world, one Code editor at a time. The main goal of the project is to show how an MCP server can be used to interact with APIs.

Enables AI assistants to interact with devpipe, a local pipeline runner, allowing them to list tasks, run pipelines, validate configurations, debug failures, analyze security findings, and generate CI/CD configs through natural language.

An educational implementation of a Model Context Protocol server that demonstrates how to build a functional MCP server for integrating with various LLM clients like Claude Desktop.

Enables execution of Go code in the Go Playground sandbox and generation of shareable URLs for code snippets. Provides tools to run Go code, share code via URLs, or perform both operations simultaneously.

An educational implementation of a Model Context Protocol server that demonstrates how to build a functional MCP server integrating with various LLM clients.

An MCP server that executes tox commands to run Python tests within a project using pytest, allowing users to run all tests or specific test groups, files, cases, or directories.

Enables creation of ephemeral, polyglot development environments using Nix for on-demand execution of code with specific dependencies across Python, Node.js, Ruby, R, and Lua without cluttering the system.

Audits npm package dependencies for security vulnerabilities, providing detailed reports and fix recommendations with MCP integration.

Converts Figma designs to React Native components, allowing users to extract components from Figma designs and generate corresponding React Native components with proper typing and styling.

A demonstration MCP server that provides a simple addition tool for learning how to create and deploy servers following the Model-Context-Protocol specification. Serves as a basic example for developers getting started with MCP server development.

Enables an MCP-compatible AI client to author Blender Python, run Blender, import the resulting asset into Unity with materials and a prefab, place it in the active scene, verify the import, and revise the same asset using stable IDs.

A vulnerable MCP server implementation that demonstrates how poor coding practices can lead to security issues like Remote Code Execution, designed for educational purposes to add numbers.

Enables AI coding agents to debug Python projects by running pytest, extracting failure locations, displaying code context around failures, and optionally requesting fix suggestions from Gemini.

Seamlessly bring real-time production context—logs, metrics, and traces—into your local environment to auto-fix code faster.

Connects to SolidWorks 2022 over COM to run, test, and auto-fix VBA macros, returning structured diagnostics for iterative correction by Claude.

A Model Context Protocol (MCP) server that converts LaTeX mathematical expressions to MathML format.

A basic serverless MCP server example running on Netlify Functions using Express. Demonstrates how to deploy and run Model Context Protocol servers in a serverless environment.

MCP server for snowflake that doesn't allow write operations under any circumstances, and includes a system prompt.

Enables MCP clients such as Claude Desktop or Cursor to inspect a coffee-shop sales database schema, validate SQL, and run guarded read-only queries. It supports natural-language questions by exposing tools for schema description, SQL checking, and safe query execution.

A simple demonstration MCP server that provides a basic arithmetic tool for adding two numbers together, showcasing how to build custom MCP servers with input validation.

Connects ChatGPT to Roblox Studio's built-in MCP, enabling natural-language reading and writing of game data, including executing Luau code and modifying scripts and instances.

A basic example of developing and running serverless Model Context Protocol (MCP) using Netlify Functions, demonstrating how to deploy and access serverless functions with customized URLs.