"Scratch" matching MCP servers:

Enables AI agents to build, playtest, and publish Scratch projects headlessly over MCP, with 112 tools covering the website/social graph, block-level project editing, headless VM execution, and Python-to-blocks workflows.

An MCP server that exposes the markdown issue tracker under .scratch/ as a queryable graph, letting agents see open, blocked, or takeable issues without re-parsing prose.

Enables AI agents to validate wireless communication ideas by providing standardized channel instances and physical measurements through MCP, eliminating the need to write simulations from scratch.

Enables AI clients to create, edit, run, and inspect ScratchJr Desktop projects using natural language, including characters, backgrounds, text, sounds, and programming blocks.

A Python MCP server implemented from scratch, providing tool execution capabilities like system time and file hashing via stdio or HTTP transport.

Lets users discover, compare, and audit public MCP servers from the official registry, validate server.json metadata, and generate installation snippets and publication checklists.

Enables AES-256-CBC encryption and decryption of messages with passphrase-derived keys, exposing tools, resources, and prompts for MCP clients like VS Code Copilot Chat.

Generates crash-proof Python LLM pipelines from plain English descriptions. Pipelines resume from the last successful node after a crash instead of restarting from scratch. IProvider-agnostic , works with Groq, Gemini, Ollama, OpenAI, or any Python callable. Zero infrastructure, runs entirely on free-tier APIs.

Enables coding agents to compose, tune, render, mix, and audit native VOCALOID3/4 projects from scratch, acting as a production bridge between intent and finished song.

MCP server providing persistent memory and context for AI tools, including semantic memory, knowledge graph, and session history to avoid starting from scratch in every conversation.

A demonstration MCP server providing basic tools for returning greetings and performing mathematical addition. It serves as a practical workshop guide for setting up and configuring Model Context Protocol servers from scratch using Python.