# Workflow

MCP Kanban is a task management toolset designed for AI agents. It manages multi-session workflows through a kanban system, supports column capacity limits, an embedded database, and Web UI monitoring. It provides API interfaces for creating, moving, and deleting tasks, as well as preset prompt words to assist in project initiation and advancement.

An example project of an MCP server demonstrating how to chat with DeepSeek and use MCP tool calls.

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.

The UNS-MCP Server is an MCP server implementation for interacting with the Unstructured API, providing various tools to manage data sources, workflows, and jobs.

Kanban MCP is a project that connects the Planka kanban board with the Cursor MCP protocol, enabling AI assistants to manage kanban tasks, including creating/moving cards, tracking time, adding comments, etc., to achieve human-machine collaborative workflow management.

This project manages the configuration rules of AI agents through the Cursor IDE, including rule editing and synchronization mechanisms, and is mainly applied to the workflows of professional agents and development agents.

An MCP server based on the CrewAI framework, providing AI agent and task management functions, supporting the creation of agents, tasks, and workflows, and running automatically.

Agentis MCP is a flexible multi - agent framework for building powerful AI agents, supporting MCP server connection, and providing functions such as tool access, resource retrieval, and multi - agent workflow orchestration.

This project is a ComfyUI workflow pipeline wrapper designed for Open WebUI. It implements image generation functionality through the comfy - mcp - server and needs to be used in conjunction with ComfyUI and Open WebUI Pipelines.

An implementation of the Unstructured API MCP server, providing tools for interacting with the Unstructured API, including operations such as creating, updating, and deleting data sources, workflows, and tasks.

AI Session Handover Management Service

An MCP service that integrates Linear task management and TrackingTime time tracking, automating the workflow through natural language.

This is an AI development tool project based on Docker containers. It uses Markdown files to write complex workflows, combines Dockerized tools with the LLM model selected by the user, and implements a workflow of multi - model agents and project - priority design.

The Apple MCP toolset is a collection of native Apple tools designed for the MCP protocol, supporting multiple functions such as messages, notes, contacts, emails, reminders, and calendars.

This project implements an MCP server that integrates Venice AI's image - generation capabilities, providing an interactive image - generation service with an approval/regeneration workflow and supporting the generation of multi - view 3D views through the Gemini API.

Data Agents is a GenAI - based data engineering agent platform that automates data workflows through multi - agent collaboration, supports multiple AI backends and n8n integration, and provides a modern UI interface and Docker deployment solution.

This is an MCP server that provides integration for Linear issue tracking and project management, supporting functions such as creating and searching for issues, managing sprints, and teams.

MCP is an open protocol for standardizing the context of AI applications, similar to the USB - C interface in the AI field, enabling seamless connection between LLMs and various data sources/tools. It provides preset integrations, multi - model switching, secure data processing, and a standardized interface, supporting the construction of LLM - based intelligent agents and complex workflows.

MCP service integration for the Productboard API

An MCP server that provides city weather information, realizing weather data query and natural - language processing functions through APIs and tool - based workflows.

00_master_mcp is the core coordinator of the MCP multi - agent system, responsible for managing the connections of all downstream service containers, context routing, security policy enforcement, and complex workflow orchestration, and providing a unified SSE endpoint for external tools to access.

A collection of MCP server examples provided by AWS GCR, covering various functions such as remote desktop, workflow services, AI planning, time services, and HTML rendering

Azure AI Vision Face Liveness Detection MCP Server for embedding proof of existence in Agentic AI workflows.

Quick-start Auto MCP is a tool that helps users quickly register the Anthropic Model Context Protocol (MCP) in Claude Desktop and Cursor. It provides functions such as RAG document search, Dify workflow, real-time web search, and supports one-click generation of JSON configuration files.

This project has configured GitHub Actions workflows for testing Plane.so API endpoints, including unit tests and integration tests. You need to configure API keys and the workspace slug as keys.

A Golang-based MCP-compatible server for running Argo workflows

The Razorpay MCP Server is an official payment integration service that provides seamless docking with the Razorpay API through the Model Context Protocol (MCP), supporting multiple payment tools and automated processes.

The ComfyUI MCP Server is a service implementation for integrating ComfyUI with MCP. It needs to be used with a running ComfyUI server and provides multiple built-in tools such as text-to-image generation, image downloading, and running custom workflows. It supports running in UV or Docker mode.

mcp - agent is a lightweight AI agent framework based on the Model Context Protocol (MCP), which supports building efficient agent applications through a simple and composable model. It integrates the lifecycle management of the MCP server and implements multiple workflow models, such as parallel processing, routing, evaluation optimization, etc., and is suitable for various AI application scenarios such as multi - agent collaboration and human - machine interaction.

A server based on the FastMCP framework that generates images according to prompts through a remote Comfy server.