MCP Kanban Memory

Created By
eyalzha year ago
MCP server providing kanban-based task management memory for complex multi-session workflows with AI agents
Overview

What is MCP Kanban?

MCP Kanban is a task management tool that utilizes a kanban-based system to manage complex multi-session workflows with large language models (LLMs). It allows AI agents to document their work in a structured manner, enhancing visibility and organization.

How to use MCP Kanban?

To use MCP Kanban, start by cloning the repository and following the installation instructions. You can create a kanban board using predefined prompts or ask the LLM assistant to record its plan. To continue work on a project, locate the specific kanban board and resume from where you left off.

Key features of MCP Kanban?

  • Kanban board for task management
  • Column capacity and work-in-progress limits
  • Embedded SQLite database for data storage
  • Web UI for monitoring workflow progress
  • Predefined prompts for project initiation and continuation

Use cases of MCP Kanban?

  1. Managing software development projects with multiple tasks.
  2. Organizing research workflows that require iterative sessions.
  3. Facilitating team collaboration on complex projects by visualizing task progress.

FAQ from MCP Kanban?

  • Can I use MCP Kanban for any type of project?

Yes! MCP Kanban is versatile and can be adapted for various project types, including software development, research, and more.

  • Is there a web interface for MCP Kanban?

Yes! MCP Kanban includes a web UI that allows users to observe and manage their workflows easily.

  • What technologies does MCP Kanban use?

MCP Kanban is built using TypeScript and utilizes SQLite for its embedded database.

Project Info
Created At
a year ago
Updated At
a year ago
Author Name
eyalzh
Star
1
Language
TypeScript
License
MIT license

Recommend Servers

View All
Bring your real authenticated browser session to AI coding agents. Local-first MCP server + Chrome MV3 extension. No cloud. No telemetry.
@Cubenest

peek records the user's actual logged-in browser (DOM via rrweb, console events, network metadata, optional response bodies via opt-in Deep capture) through a Chrome MV3 extension. The extension ships events through a native-messaging stdio bridge to a local MCP server (peek-mcp), which persists them to a SQLite database at ~/.peek/sessions.db. AI coding agents (Claude Code, Cursor, Cline, Windsurf) read sessions from the database via 10 MCP tools: Tool What it does list_recent_sessions List recently recorded sessions (id, origin, ts, event count). get_session_summary LLM-readable narrative summary of a session. get_session_console_errors Console errors recorded in a session. get_session_network_errors Failed/notable network requests in a session. get_user_action_before_error Last N user actions before a console error. generate_playwright_repro Generate a runnable Playwright test from a session. get_dom_snapshot Reconstruct the DOM at a given timestamp. query_dom_history Timeline of attribute/text changes for a selector. request_authorization Side-panel consent for write actions (Level 3). execute_action Dispatch a UI action (gated by permission level + destructive blocklist). Why local-first matters Every other "browser session for AI" tool ships to a vendor cloud. peek's SQLite + extension live on the user's machine — no remote endpoints, no telemetry. The privacy policy (docs/peek/PRIVACY_POLICY.md) is the source of truth. Install # 1. Add the MCP server to Claude Code claude mcp add peek -- npx -y @peekdev/mcp # 2. Install the Chrome extension from the Chrome Web Store # (link added once the CWS listing is approved)

2 days ago
Voyei

4 hours ago