"Scratch" matching MCP servers:

Enables AI agents to create, compile, and run Scratch projects by editing plain text and using a live editor loop.

Enables creating real Scratch 3 projects from natural language prompts, producing editable .sb3 files with sprites, scripts, sounds, and backdrops.

An MCP server that enables AI assistants to analyze scratch-off lottery games across 19 U.S. states, with tools for account management, game browsing, and subscription.

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 creating, editing, inspecting, rendering, and live-previewing Rive (.riv) animations directly from Claude without needing the Rive editor or cloud subscription, including building .riv files from scratch and a local web studio for human-AI collaboration.

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.

MCP server for FreeVectorIcons design collections, enabling agents to search and fetch approved icons and assets instead of generating SVG from scratch.

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.

Provides AI coding agents with structured, deterministic repository intelligence—file tree, dependencies, hotspots, and git history—via Model Context Protocol, enabling them to understand codebases without reconstructing architecture from scratch.

Enables shared scratchpad functionality for Claude Code sub-agents with full-text search, workflow organization, and intelligent Chinese text tokenization. Supports seamless context sharing and collaboration across workflows with project scope isolation.

Shared rooms for AI agents (AgentsChat): channels, DMs, proposals & voting, OKR trees, and human handoff. Existing MCP clients (Claude Code, Cursor, and others) join live rooms instead of building a crew from scratch.

Enables coding agents to search, inspect, and retrieve real UI components from existing component libraries, with ranked results, score explanations, licence and provenance details, and safe installation plans. It also surfaces similar components and higher-level UI patterns so agents can reuse existing interface code instead of generating it from scratch.

An MCP server that provides deep knowledge about OpenAI APIs and SDKs, enabling users to query technical information through various MCP clients including ChatGPT Deep Research, Cursor, and OpenAI Responses API.

scratchpad-mcp is an MCP server that gives AI agents persistent, token-efficient storage. Eight tools versioned file writes, structured line-diffs since a prior version, append-only logs with cursor pagination, on-demand summaries with per-version caching, and per-agent quotas. The point is to stop agents from re-reading files they've already seen and re-summarizing documents.

Gives Claude Code sessions a durable, shared memory and work ledger backed by a local SQLite database, exposing the brain_* tools so agents can create and lock goals with verifiable success criteria, plan and track work units, record evidence and failures, and search decisions, learnings and project knowledge. Because the tool descriptions carry the operating rules, every session in every project reads and writes the same accumulating context instead of starting from scratch.

Give your coding agent the repo context it needs before it starts searching. Madar builds a local graph of your TypeScript or Node.js repository and turns the current question into a small, task-aware context pack. Claude Code, Codex, Cursor, Copilot, Gemini, Aider, and OpenCode can start from relevant files, symbols, snippets, and relationships instead of rediscovering the repository from scratch

Enables structured input surfaces inside Claude conversations with prefilled forms, assumption ledgers, and review matrices, so the agent can infer values and present proposals for auditing rather than authoring from scratch.