An intelligent task management system based on the MCP protocol, providing an efficient programming workflow framework for AI agents, with functions such as task planning, decomposition, execution, and memory.

Bonsai - mcp is a Blender plugin that implements the analysis and operation of IFC building models through IfcOpenShell, supporting the execution of building information model query tasks through interaction with LLMs such as Claude.

A Claude task scheduling server based on the MCP protocol, supporting the scheduled execution of AI tasks through cron expressions, providing a Web dashboard, Webhook triggers, dynamic MCP server creation, and cost tracking functions.

An AI agent and task management server based on the CrewAI framework, providing functions such as agent creation, task assignment, and workflow execution.

Nash MCP is a multi - functional command execution server that supports Shell command execution, Python code execution, web content fetching, and task management, providing secure credential storage and session management functions.

An MCP service based on the Gradle Tooling API that supports querying Gradle project information, executing tasks, and running tests in a structured way.

MCP TaskManager is a queue - based task management system that provides task planning, execution, and completion feedback functions for MCP clients such as Claude Desktop.

gbox is an open - source self - hosted sandbox project that provides a secure environment for MCP integration and other AI agent use cases. It includes tools such as a terminal, browser, and file editor, supports local task execution and file processing, and provides Python and Typescript SDKs.

Sugar is an autonomous execution layer designed for AI programming agents, providing task queue management, 24/7 continuous operation, and a persistent memory system, supporting integration with mainstream AI coding tools such as Claude Code and OpenCode.

Overview: The MCP server acts as middleware, enabling AI agents to automate tasks by connecting to local data and remote services. It supports functions such as query execution and prompting to efficiently handle context. This represents a step towards fully automated AI systems capable of performing complex operations.

MCP Cron is a task scheduling server based on the MCP protocol. It supports scheduling Shell commands and AI tasks through cron expressions and provides task management and execution functions.

Rube is an MCP server based on the Composio platform that connects AI chat tools to over 500 business and productivity applications, enabling automated task execution through natural language commands.

Orchestro is an AI development coordination platform that connects product managers, developers, and AI, transforming product ideas into production code. It coordinates the entire development process through task decomposition, dependency tracking, pattern learning, and real-time progress visualization, providing functions such as project management, task execution, knowledge base, and real-time dashboard.

Sequential Thinking MCP is an MCP server that supports AI agents in advanced metacognition and dynamic reflective problem-solving, guiding the step-by-step execution of complex tasks through virtual thought recording and planning.

Agent Farm v3.4 is a system based on AI agent evolution and parallel task execution, which realizes task processing through tool - enhanced agents. The new version introduces the chunked write mode, supports the parallel generation of large documents and code files, improves performance by 8.6 times, and uses local models for result synthesis without relying on cloud tokens.

This is a server based on the Model Context Protocol (MCP) for executing SQL queries through the Databricks Statement Execution API, supporting complex task iteration and Unity Catalog metadata integration.

A VSCode extension based on the FastMCP framework that transforms VSCode into an MCP server, supporting remote execution of internal VSCode commands via HTTP Streaming, querying workspace information, and asynchronous task management

The combination of Gemini CLI and the MCP server built with Google Apps Script extends the automation capabilities of Google Workspace through a low - code platform, providing support for 41 tools covering services such as Gmail, Drive, Calendar, etc., and enabling multi - step execution of complex tasks.

MyMCP is a Python-based application that supports parallel execution of multiple AI coding tasks through the Aider tool, providing options for parallel/sequential execution, detailed reports, and performance comparison functions.

This project is an automation platform integrating AI, Ansible, and OpenShift. It enables interaction with Claude Desktop through the MCP server and supports automated task execution and management.

Tiger Skills MCP Server is a server based on the Model Context Protocol (MCP), aiming to enhance the professional capabilities of AI agents through modular skills (Skills). It supports the configuration of local and GitHub skill libraries and provides sub - agent task execution functions.

Skynet - MCP is a hierarchical AI agent network project based on the Model Context Protocol (MCP), supporting the creation of sub - agents to process complex tasks in parallel, integrating multiple AI models and service tools, and providing functions such as dual - mode operation, tool discovery, and asynchronous execution.

An MCP server based on OpenAI's text-to-speech API, used to report task status or confirm command execution to users via voice messages.

SuperAgent is an MCP server that uniformly calls the Codex, Gemini, and Continue CLI agents. It supports parallel task execution, manages preset system prompts, and provides structured tool calls to clients through the MCP protocol.

JitAPI is an MCP server that enables LLMs to interact with any API by dynamically discovering relevant endpoints in the OpenAPI specification. It uses semantic search and dependency graphs to find only the endpoints required for each task, supporting multi-API registration, LLM-driven workflow planning, and general execution.

A multi - agent browser automation framework based on AgentKit, which realizes intelligent web page navigation and task execution through division of labor and collaboration, including four core modules: task planning, navigation control, browser operation and result verification.

An AI-native execution system based on Claude and the MCP protocol, which manages tasks, projects, and goals through natural language interaction, eliminating the context-switching friction caused by traditional productivity tools

A Python-based MCP server for guiding agents to execute tasks according to predefined structured workflows while maintaining execution flexibility.

Mission Control MCP is a task automation and verification system designed specifically for Claude Code, providing autonomous task execution, result verification, and iterative optimization functions. It supports multiple verification strategies, has no external dependencies, and focuses on privacy and security.

mcp-core is a core monorepo for building intelligent language-driven backend services, supporting a modular MCP ecosystem and enabling natural language command parsing and task execution.

Overview: MCP is a standard protocol, similar to the Type C protocol of electronic devices (which can both charge and transmit data), enabling AI models to interact with different APIs and data sources. It aims to replace fragmented Agent code integration and improve the reliability and efficiency of AI systems. By establishing a common standard, service providers can offer their own AI capabilities according to the protocol, supporting developers to build more powerful AI applications more quickly. MCP maintains the context between different applications and services, enhancing the overall ability of autonomous task execution.

Fetcher MCP is an MCP server based on the Playwright headless browser, specifically designed for fetching web page content. It supports JavaScript execution, intelligent content extraction, parallel processing, and multi-format output, and is suitable for handling dynamic web pages and batch fetching tasks.

The MCP Scheduler is a task scheduling server based on the Model Context Protocol, supporting the scheduled execution and management of multiple task types (Shell commands, API calls, AI tasks, reminder notifications). It provides complete execution history records and flexible cron expression scheduling functions.

This project provides an intelligent planning and goal execution agent system based on ReasoningBank closed-loop learning. It supports dynamic planning, adaptive replanning, and continuous learning and improvement, and is suitable for complex multi-step task deployment scenarios

Moonbridge is an MCP server that allows multiple AI coding agents (supporting backends such as Kimi, Codex, OpenCode, Gemini) to be generated in parallel from MCP clients such as Claude Code and Cursor, used to try multiple code solutions simultaneously, enabling parallel exploration and task execution at a lower cost.

A Rust-based MCP server that provides autonomous wake-up, heartbeat, and distributed task execution functions for Claude Desktop through AppleScript, supporting macOS scheduled scheduling and state management.

The Sleep MCP Server is an MCP server for controlling the agent execution flow, which realizes the orderly execution between tasks by setting delay times.

A task management API based on TypeScript and WebSocket, supporting modular command execution and AI agent integration, using the SQLite database.

The MCP Background Job Server is a Python - based asynchronous task execution system that supports managing long - running Shell commands through the MCP protocol, providing process lifecycle management, real - time output monitoring, and interactive operation functions.

MCPlanManager is a task management system designed for AI Agents, supporting the MCP standard and providing functions such as task plan initialization, execution, persistence, and visualization. It supports two deployment methods: uvx and Docker.