An MCP server that provides shared memory, kanban board, and agent registry for AI agents to collaborate as a team, with a live dashboard for human oversight.
MCP server that provides a real-time project management board with cards, sprints, comments, and docs, enabling coding agents to query and edit project state.
A multi-agent product-management copilot that automates morning briefings, metrics answers, knowledge lookups over an Obsidian vault, and PRD/ticket drafting.
Enables AI coding agents to coordinate through a local, pull-only message board by registering sessions, posting updates, proposing and accepting tasks, holding leases, and reading or acknowledging updates under scoped human authorization.
MCP server that connects AI assistants to Instructure Canvas LMS, enabling teachers and administrators to manage courses, assignments, grades, and more through natural language.
Enables MCP-compatible coding agents to share persistent project context, task claims, handoffs, decisions, test reports, and searchable history through a local or team-hosted SQLite-backed service with a React dashboard.
MCP server that enables AI agents to manage Dida365/TickTick tasks, projects, tags, and habits through natural language, with full CRUD, search, and advanced V2 capabilities.
Enables AI agents to create and manage small project plans—including tasks, assignees, status, progress, dates, and dependencies—and retrieve Gantt-friendly project views over MCP stdio. State is stored in SQLite without external dependencies.
Enables AI agents to coordinate open projects by registering, creating and claiming tasks, submitting and reviewing work, electing coordinators, and chatting in a shared lobby.
Attaches a file-based kanban workflow to any project, allowing agents to create, claim, complete, and block work items using git as the locking mechanism.
Enables AI agents to maintain project continuity through a file-based state hub with tasks, phases, and handoff snapshots. Provides MCP tools for reading and updating project state, with gatekeeping enforced via real-state evaluation and per-tool authorization.
Enables any MCP-capable coding agent to share one plain-Markdown project state — goals, tasks, decision logs, and session handoffs — stored in a project's .apm/ directory, with lock-safe concurrent writes and consistency checks that flag stale, blocked, or unowned work. This lets multiple agents and a human CLI resume work across interrupted sessions without a database or external service.
Enables people and their AI agents to share a kanban board whose tasks, hand-offs, reviews, notes and alerts live as markdown files in a private GitHub repo, with per-person access control. Works from Claude, ChatGPT, Claude Code, Codex or any MCP-speaking client, and can draw the board inside chats.
An MCP server enabling AI coding agents to interact with a task tracker that enforces rules server-side, replacing markdown files and hooks with a database, CLI, and web UI.
A task orchestration system for AI agent teams that provides a Kanban dashboard and 12 MCP tools to manage multi-step workflows with DAG dependencies. It enables agents to coordinate through task threads, automated retries, and real-time webhook notifications.
Enables pipeline-driven task management for AI coding agents, with stage-gated workflows, dependency tracking, artifact versioning, and multi-agent collaboration.
Enables AI coding agents on the same machine to exchange local messages, manage and claim tasks, and coordinate folder locks through stdio MCP tools backed by plain files. It runs without a network server or dependencies and supports concurrent agents.
Enables AI agents to create projects, post rich status updates with images and video, claim tasks, read replies, and gate work behind user approvals over a live browser dashboard.
Enables agents to manage a local SQLite task backlog over stdio, with tools to create, search, list, get, update, and stat tasks while preserving audit history and idempotency.
Provides a local, vendor-agnostic control room where coding agents can be assigned tasks, exchange scoped messages, submit claims, and have work reviewed across different models, with a voxel-world interface for inspecting sessions and evidence.
An AI-native kanban board MCP server where agents pull tasks via the Model Context Protocol. It provides tools for task discovery, claiming, progress updates, and review workflows.
An MCP host that integrates a CLI agent with local todo servers over stdio and authenticated Streamable HTTP, supporting progressive skill loading for task management and daily planning workflows.
A read-only MCP server that syncs only the Telegram chats, folders, and forum topics you explicitly mark (via Telegram folders or watch.py) into a local SQLite index using Telethon, then exposes 13 read-only tools to an agent. Enables listing watched topics with unread counts, fetching new messages since last read, paging and date-filtered history retrieval with context budgets, full-text search over years of history (SQLite FTS5/bm25), activity stats, link/mention extraction, digests, and extracted entities such as tasks, decisions, promises, and questions with overdue tracking — all through user-defined aliases. No tool writes to Telegram, keeping the integration strictly read-only.
Enables Claude to interact with Jira, Salesforce, and n8n through MCP tools, allowing automated reading of Jira stories, deploying Salesforce metadata, and triggering n8n workflows.
Exposes GitHub issue and pull-request triage capabilities to any MCP client: fetching repository context, classifying and prioritizing requests, finding duplicates, and applying labels, comments, assignments, closures, or reopenings. All writes are policy-gated, dry-run by default, and destructive actions require explicit two-step human confirmation.
An MCP server that connects AI assistants to Notion, enabling automated research, GitHub tracking, and content pipelines with human-in-the-loop approval via a Notion task queue.
A Model Context Protocol server for AI agents to manage tasks and track progress across projects, with features like project isolation, search, and reporting.