"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.

Enables clients to interact with a bundled documentation corpus through MCP tools and resources over HTTP or stdio, and provides a trajectory evaluation harness for grading agent performance.

Enables learning and integration testing of the Model Context Protocol by evolving from a minimal server toward production-grade tools, resources, prompts, and agent interoperability.

A Flask-based MCP server designed for testing deployment on Google App Engine. Provides a deeplink checking endpoint that accepts flattened JSON parameters and forwards them as nested objects to external APIs.

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.

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.

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.

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.

Provides a 'think' tool that allows Claude and other LLMs to add dedicated thinking steps during complex tool use scenarios, creating space for structured reasoning and improving problem-solving capabilities.

A simple MCP server that exposes stock price lookup tools, demonstrating how to set up MCP from scratch with Ollama.

Manages a global scratchpad file on your Desktop for tracking interruptions, ideas, tasks, and focus across all projects with secure input validation and rate limiting.

MCP server for Databricks Unity Catalog, enabling table listing, schema description, and read-only SQL query execution via natural language. Built from scratch to demonstrate JSON-RPC 2.0 protocol depth.

A local MCP server that guides users through Linux From Scratch (LFS) documentation step by step, tracking progress in a SQLite database and providing search, sequential navigation, and optional AI chat assistance.

Connects to Salesforce scratch orgs and allows AI to generate and insert realistic nested test records via natural language descriptions using the Composite Tree REST API.

An educational MCP server that teaches implementing the Model Context Protocol from scratch, enabling AI models to discover and invoke tools via JSON-RPC over stdio.

A minimal MCP server built from scratch with no SDK, providing shell, file read/write, and URL fetch tools over Streamable HTTP and WebSocket.

Provides persistent agent workspaces with MongoDB storage for file management and real-time task progress tracking. It enables secure, isolated user environments with full activity logging for workspace operations.