Planka MCP Server

# 🧠 MCP Server Capabilities and LLM Interaction Strategies This guide focuses on the capabilities of the MCP Kanban server specifically and how Large Language Models (LLMs) can effectively interact with it to manage Planka kanban boards. ## 🛠️ MCP Server Capabilities The MCP Kanban server is a specialized middleware designed to facilitate interaction between LLMs and Planka. It serves as an intermediary layer that provides LLMs with a simplified and enhanced API to interact with Planka's task management system. ### 🔄 Core MCP Server Functions 1. **🧩 Context-Aware API Wrapping**: The MCP server wraps the Planka API with specialized functions optimized for LLM interaction, combining related operations and adding context awareness. 2. **🔝 Higher-Level Operations**: Rather than requiring LLMs to make multiple low-level API calls, the MCP server provides higher-level functions that abstract complexity. 3. **📊 Structured Workflow Support**: The server enables LLMs to follow a structured approach to software development using Kanban methodology. 4. **💾 State Persistence**: The MCP server maintains state between interactions, allowing LLMs to pick up where they left off even across different chat sessions. 5. **🔐 Authentication Management**: The server handles authentication with Planka, simplifying the interaction for LLMs. ### 🌟 Key Capabilities for LLMs The MCP Server enables LLMs to: 1. **📋 Access Kanban Data**: Retrieve projects, boards, lists, cards, tasks, comments, and labels from Planka. 2. **🔄 Manage Workflow**: Move cards between lists (Backlog → In Progress → Testing → Done). 3. **✅ Track Tasks**: Create, update, and complete tasks within cards. 4. **💬 Communicate Progress**: Add comments and labels to cards to document progress and status. 5. **⏱️ Monitor Time**: Start, stop, and reset stopwatches to track time spent on cards. 6. **🔄 Duplicate Work**: Copy existing cards to create templates or reuse configurations. 7. **📈 Follow Task-Oriented Development**: Support a structured approach to software development using Kanban methodology. ## 🤖 LLM Interaction Strategies ### 🔄 Task-Oriented Development Workflow The MCP server is designed to support a specific task-oriented development workflow for LLMs: 1. **🔍 Identify Next Task**: The LLM can identify the next card to work on from the Backlog. 2. **▶️ Start Work**: Move the card to In Progress and start the stopwatch. 3. **🛠️ Implement Tasks**: Implement the required tasks one by one. 4. **📝 Document Progress**: Add comments to document progress. 5. **⏹️ Complete Work**: Stop the stopwatch and move the card to Testing when complete. 6. **📋 Process Feedback**: Process human feedback from Testing. 7. **✅ Finalize**: Either address feedback or move the card to Done. This structured approach ensures that the LLM can effectively manage software development tasks while maintaining clear communication with human team members through the Kanban board. ### 🤝 LLM-Driven Development with Human Review In this workflow, the LLM handles most of the implementation work while humans focus on review and direction: 1. **👤 Human**: Creates high-level cards in the Backlog with basic requirements. 2. **🤖 LLM**: - Grooms the cards by breaking them down into specific tasks - Moves cards to In Progress and implements the tasks - Tracks time spent on implementation - Documents progress with detailed comments - Moves completed cards to Testing 3. **👤 Human**: - Reviews the implementation in Testing - Provides feedback as comments - Approves or requests changes 4. **🤖 LLM**: - Addresses feedback by moving cards back to In Progress if needed - Implements requested changes - Moves cards to Done when approved **Benefits**: Maximizes developer productivity by offloading implementation details to the LLM while maintaining human oversight for quality control. ### 👥 Human-Driven Development with LLM Support In this workflow, humans handle the core implementation while the LLM provides support: 1. **🤖 LLM**: - Grooms the Backlog by analyzing requirements and breaking them into tasks - Provides technical recommendations and implementation approaches 2. **👤 Human**: - Selects cards to work on and moves them to In Progress - Uses stopwatches to track time spent on tasks - Implements the tasks - Moves completed cards to Testing 3. **🤖 LLM**: - Reviews the implementation - Provides feedback on code quality, potential issues, and improvements - Documents the review as comments 4. **👤 Human**: - Addresses feedback - Moves cards to Done when complete **Benefits**: Leverages the LLM's ability to analyze requirements and review code while keeping humans in control of the implementation. ### 🧠 Collaborative Grooming and Planning In this workflow, humans and LLMs collaborate on planning and organizing work: 1. **👤 Human**: Creates basic card ideas in the Backlog. 2. **🤖 LLM**: - Analyzes the codebase to provide context - Breaks down cards into specific tasks - Identifies dependencies and potential challenges - Suggests implementation approaches - Estimates time requirements for tasks 3. **👤 Human**: - Reviews and refines the groomed cards - Prioritizes the Backlog - Assigns cards to team members or the LLM 4. **👥 Both**: Implement cards according to their assigned responsibilities. **Benefits**: Improves planning quality by combining human domain knowledge with the LLM's ability to analyze the codebase and identify implementation details. ## ⏱️ Time Tracking Capabilities The MCP Kanban server includes robust time tracking features that enable both LLMs and humans to monitor and analyze time spent on tasks: ### 🔄 Stopwatch Functionality 1. **▶️ Start Tracking**: Begin monitoring time for a specific card ``` LLM: startStopwatch(cardId) ``` 2. **⏸️ Pause Tracking**: Temporarily stop the timer without resetting ``` LLM: stopStopwatch(cardId) ``` 3. **🔄 Resume Tracking**: Continue tracking from where you left off ``` LLM: startStopwatch(cardId) ``` 4. **🔁 Reset Timer**: Clear tracked time and start fresh ``` LLM: resetStopwatch(cardId) ``` 5. **👁️ Check Status**: View current tracking status and elapsed time ``` LLM: getStopwatch(cardId) ``` ### 📊 Time Management Strategies 1. **🎯 Task Estimation and Validation**: - LLMs can estimate time requirements for tasks - Actual time tracking validates these estimates - Helps refine future estimates based on real data 2. **📈 Productivity Analysis**: - Identify which tasks are taking longer than expected - Track patterns in time usage across different task types - Recommend workflow optimizations based on time data 3. **🔍 Focus Management**: - Help developers maintain focus on one task at a time - Provide visibility into context switching - Encourage time-boxing for enhanced productivity 4. **📑 Reporting**: - Generate time-based reports for sprint retrospectives - Provide insights into team velocity - Support data-driven project management decisions ## 🏆 Key Advantages for LLMs ### 💾 Context Retention Across Sessions One of the key advantages of using MCP Kanban with LLMs is context retention across different chat sessions: 1. **📝 Card State**: The LLM can retrieve the current state of any card, including its description, tasks, comments, and history. 2. **⏱️ Time Records**: Track time spent across multiple work sessions with persistent stopwatches. 3. **🔄 Work Continuity**: Even if a developer starts a new chat session, the LLM can pick up exactly where it left off by checking the Kanban board. 4. **📚 Knowledge Transfer**: New team members can quickly get up to speed by having the LLM explain the current state of the project based on the Kanban board. 5. **📋 Documentation**: The Kanban board serves as living documentation of the project's progress and decisions. ### 🎯 Task-Focused Development The MCP Kanban system helps keep LLMs focused on specific tasks: 1. **📊 Clear Priorities**: The Kanban board makes it clear which tasks are highest priority. 2. **🔄 Structured Workflow**: The board enforces a consistent process from Backlog to Done. 3. **📈 Progress Tracking**: Time tracking and task completion provide visibility into progress. 4. **🧠 Reduced Context Switching**: By focusing on one card at a time, LLMs can maintain focus and productivity. ## 📋 Implementation Guidelines for LLMs To get the most out of the MCP Kanban server, LLMs should: 1. **🔍 Check Board State First**: Always check the current state of the board before taking action. 2. **🔄 Follow the Workflow**: Respect the Kanban workflow (Backlog → In Progress → Testing → Done). 3. **📝 Document Actions**: Add comments to cards to explain what was done and why. 4. **🧩 Break Down Work**: Create small, focused tasks rather than trying to implement everything at once. 5. **📋 Provide Context**: When creating or updating cards, include sufficient context for humans to understand. 6. **🔄 Respond to Feedback**: Actively look for and address feedback from human team members. 7. **⏱️ Use Time Tracking**: Start and stop stopwatches to monitor time spent on tasks. ## 🔧 Technical Integration The MCP Kanban server is designed to be used with Cursor's MCP system: 1. **🐳 Docker-Based**: The server runs in a Docker container for easy deployment. 2. **⚙️ Configuration via Environment Variables**: The server is configured using environment variables. 3. **🔌 Cursor Integration**: The server integrates with Cursor's MCP system to allow LLMs like Claude to interact with it. 4. **🌐 Host Communication**: The server communicates with the host Planka instance using HTTP. ## ❓ Troubleshooting for LLMs If an LLM encounters issues with the MCP Kanban server: 1. Check if the server is running and accessible. 2. Verify that the authentication credentials are correct. 3. Check if the requested resource (project, board, list, card) exists. 4. Try breaking down complex operations into simpler steps. 5. Look for error messages in the response and address the specific issue. For more detailed information on specific commands, refer to the [API Reference](API-Reference) page.