Octomind provides an end-to-end testing platform, and the MCP service supports localized test management

The Linear Regression MCP project demonstrates an end-to-end machine learning workflow using Claude and the Model Context Protocol (MCP), including data preprocessing, model training, and evaluation.

The MCP Server of Debugg AI is an AI-driven browser automation testing tool that supports end-to-end testing through natural language and CLI. It can remotely manage browser tests without configuration and is suitable for local development or CI/CD processes.

A UI/UX design automation suite based on the MCP protocol that enables end-to-end workflows from design inspiration to development and delivery through AI assistance. It includes three core functions: web inspiration collection, Figma design integration, and React code generation.

This project implements an MCP server based on the SSE transport layer and Durable Objects, supports integration with the ModelContextProtocol TypeScript SDK, and provides end-to-end operation steps.

The Octomind MCP server provides localized end-to-end test management services

This project demonstrates the training of a linear regression model using Claude and the Model Context Protocol (MCP) for an end-to-end machine learning workflow. Users only need to upload a CSV dataset, and the system can automatically complete the entire process of data preprocessing, model training, and evaluation (RMSE calculation).

MCP DevOps Hub is a server that provides end-to-end development visualization, integrating tools such as Jira, GitHub, and CI/CD, and supporting team notifications and code analysis.

The RAG Anything MCP Server is a Model Context Protocol server that provides comprehensive Retrieval Augmented Generation (RAG) capabilities. It supports multi-modal document processing and querying, and has functions such as end-to-end document parsing, batch processing, advanced querying, and persistent storage.

An MCP Server based on Playwright, focusing on automated end-to-end testing, leveraging LLM-driven test verification to separate testing from the client.

An example project showing how to perform end-to-end testing on TypeScript MCP servers using Vitest, including two testing methods: raw CLI and SDK.

The fileAI MCP server provides an end-to-end file processing solution, including OCR, document classification, and structured data extraction, and supports AI model integration and asynchronous processing.