"How to Create Looker Dashboards" matching MCP servers:

Provides MCP tools to create, run commands in, checkpoint, and delete durable cloud environments, giving coding agents persistent state between calls.

Enables agents to dynamically create, edit, call, and persist Python tools across sessions.

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

Enables natural language control of AutoCAD LT through AutoLISP code generation and execution, allowing users to create engineering drawings with conversational prompts.

Enables AI models to dynamically create and execute their own custom tools through a meta-function architecture, supporting JavaScript, Python, and Shell runtimes with sandboxed security and human approval flows.

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.

A powerful AI-driven tool that helps developers create beautiful, modern UI components instantly through natural language descriptions.

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.

MCP server enabling AI agents to programmatically connect to and control Apache Superset instances, allowing users to manage dashboards, charts, databases, datasets, and run SQL queries through natural language interactions.

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

A MCP server for managing and storing code snippets in various programming languages, allowing users to create, list, and delete snippets via a standardized interface.

An MCP server that connects directly to a Jupyter kernel via ZMQ, enabling AI assistants to read, create, edit, execute, and manage Jupyter Notebooks as MCP tools.

A dynamic service that creates and manages Model Context Protocol (MCP) servers, allowing users to spawn, customize, and control multiple MCP servers as child processes.

An experimental server implementing Model Context Protocol to automate website deployments to 1Panel servers, allowing users to create websites and upload static files through natural language commands.

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 code agents to interact with Netlify services through the Model Context Protocol, allowing them to create, build, deploy, and manage Netlify resources using natural language prompts.

A Model Context Protocol server that provides tools for code modification and generation via Large Language Models, allowing users to create, modify, rewrite, and delete files using structured XML instructions.

Enables AI coding agents to manage cloud containers, create and edit files, run commands, and deploy projects directly.

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.