A REST API, MCP server, and interactive CLI for managing tasks organized in lanes, where each task is a markdown file. The CLI includes a full-screen Textual TUI (kanban board with keyboard navigation) as well as non-interactive subcommands for scripting.
This project builds on and extends the Tasks.md project. For comprehensive information about the Tasks.md system, including task visualization, board views, file format specifications, and VSCode integration, visit the Tasks.md repository.
This package adds a REST API, a network-capable interactive CLI, and an MCP server for programmatic task management.
Tasks are stored as markdown files organized in lane directories:
- Task title = filename (without
.md) - Tags:
[tag:tagname]— one per line at the top of the file - Due dates:
[due:YYYY-MM-DD] - Tasks can be split into subtasks using the
[[split]]marker
┌─────────────┐ HTTP ┌──────────────────┐
│ tasks CLI │ ─────────────▶│ Flask REST API │ :2999 (internal)
│ (bin/tasks)│ │ (task_api/) │ :3101 (host)
└─────────────┘ └────────┬─────────┘
│
┌─────────▼─────────┐
┌─────────────┐ MCP │ task_lib/ │
│ AI assistant│ ─────────────▶│ MCP Server │ :3003
│ (Claude etc)│ │ (mcp_task_service)│
└─────────────┘ └────────┬─────────┘
│
┌─────────▼─────────┐
│ Markdown files │
│ /data/tasks/ │
└───────────────────┘
task.md-utilities/
├── bin/
│ ├── tasks # CLI — TUI (no args) or subcommands (scripting)
│ └── tag-utility.py # One-time tag format migration utility
├── docs/
│ └── screenshot.svg # TUI kanban board screenshot
├── task_lib/
│ ├── api_client.py # Shared HTTP helpers (used by CLI and TUI)
│ ├── config.py # Configuration (YAML)
│ ├── task.py # Task model and file I/O
│ └── task_manager.py # Lane and task operations
├── task_tui/ # Textual TUI package
│ ├── app.py # KanbanApp — main board, keybindings, workers
│ ├── api.py # Sync API wrappers for use in workers
│ ├── screens.py # Detail, form, confirm, filter, move screens
│ └── widgets.py # LaneColumn, TaskItem, FunctionKeyBar
├── task_api/ # Flask REST API service
│ ├── app.py
│ ├── config.py
│ ├── models.py # Pydantic request/response schemas
│ ├── routes/
│ ├── gunicorn.conf.py
│ ├── Dockerfile
│ ├── docker-compose.yml
│ └── README.md
├── mcp_task_service/ # FastMCP server for AI assistants
│ ├── server.py
│ ├── Dockerfile
│ ├── docker-compose.yml
│ └── README.md
├── tests/ # pytest suite
├── config.yaml # Task data location (baked into Docker images)
└── docker-compose.yml # Deploys both services together
Both services are deployed as Docker containers. Build from the repo root (build context must include both task_lib/ and the service directory).
docker build -t localhost:5000/task-api:latest -f task_api/Dockerfile .
docker build -t localhost:5000/task-manager-mcp:latest -f mcp_task_service/Dockerfile .
docker push localhost:5000/task-api:latest
docker push localhost:5000/task-manager-mcp:latestdocker-compose up -dThe root docker-compose.yml starts both services on a shared network:
| Service | Internal port | Host port |
|---|---|---|
| REST API | 2999 | 3101 |
| MCP server | 3003 | 3003 |
Both containers mount the task data directory:
volumes:
- /mnt/raid1/lib/tasks.md/tasks:/data/tasksconfig.yaml (committed to the repo) sets the task data location inside the container. It is copied into the image at build time — no volume mount needed:
base_dir: /data/tasksTo change the data path, edit config.yaml and rebuild the images.
Each service has its own docker-compose.yml for standalone use:
cd task_api && docker-compose up -d # REST API only
cd mcp_task_service && docker-compose up -d # MCP onlyThe tasks binary has two modes:
- No arguments — launches the full-screen interactive TUI (kanban board)
- With a subcommand — runs non-interactively for scripting
poetry install
chmod +x bin/tasksResolution order (first match wins):
--api-url URLflagTASKS_API_URLenvironment variable~/.config/tasks/config.yaml→api_urlkey- Default:
http://localhost:3101
Create ~/.config/tasks/config.yaml to set a permanent remote URL:
api_url: http://your-server:3101tasksLaunches a full-screen kanban board. Lanes are displayed as side-by-side columns; tasks are listed under each lane.
| Key | Action |
|---|---|
← / → |
Move between lanes |
↑ / ↓ |
Move between tasks within a lane |
Enter |
Open task detail view |
Esc |
Go back / close dialog |
F1 |
Show keyboard shortcut help |
F2 |
New task (in the focused lane) |
F3 |
Edit selected task |
F4 |
Delete selected task (confirm prompt) |
F5 |
Filter tasks (by lane, tag, or title substring) |
F6 |
Move selected task to another lane |
F7 |
Open / close task detail view |
F8 |
Refresh board from the API |
F9 |
Quit |
Active filters are shown in a status bar at the top; press F5 again to change or clear them.
tasks [--api-url URL] COMMAND
Commands:
show List tasks (--lane, --tag, --string filters)
get Show a single task in full
add Create a new task
update Update task fields
delete Move a task to Trash
empty-trash Permanently delete all tasks in Trash
move Change a task's lane
split Split tasks containing [[split]] marker
lanes Manage lanes
list List lanes with task counts
add Create a new lane
stats Show statistics summary
# List all tasks
tasks show
# Filter by lane, tag, or title substring (all case-insensitive)
tasks show --lane "In Progress"
tasks show --tag urgent
tasks show --string "login"
# Show full task detail
tasks get "Implement login"
# Create a task (prompts for missing fields)
tasks add --title "Fix bug" --content "Reproduce and fix" --lane Backlog --tags "bug,urgent"
# Update fields (only provided fields change)
tasks update "Fix bug" --tags "bug,urgent,p1" --due-date 2026-06-01
# Move between lanes
tasks move "Fix bug" "In Progress"
# Delete (moves to Trash, prompts for confirmation)
tasks delete "Fix bug"
# Permanently delete all tasks in Trash (prompts for confirmation)
tasks empty-trash
# Lane management
tasks lanes list
tasks lanes add "Sprint 3"
# Split a task (divides on [[split]] marker)
tasks split
# Statistics
tasks statsThe MCP server exposes kanban operations to AI assistants (Claude, Cursor, etc.) via the Model Context Protocol.
Add to your Claude Desktop / Claude Code config (~/.claude/claude_desktop_config.json or .mcp.json):
{
"mcpServers": {
"task-manager": {
"type": "http",
"url": "http://localhost:3003/mcp"
}
}
}| Tool | Description |
|---|---|
add_task |
Create a task (title, content, lane, tags, due_date) |
get_task |
Retrieve a task by title |
update_task |
Update task fields |
delete_task |
Move a task to Trash |
move_task_to_lane |
Move a task to a different lane |
list_tasks |
List tasks (lane and tag filters, case-insensitive) |
list_lanes |
List lanes with task counts |
add_lane |
Create a new lane |
split_tasks |
Split tasks with [[split]] marker |
empty_trash |
Permanently delete Trash contents |
get_statistics |
Lane, tag, and due-date statistics |
Title and tag matching is case-insensitive across all tools.
See mcp_task_service/README.md for full details.
The REST API is documented in task_api/README.md.
Quick reference:
# Health check
curl http://localhost:3101/health
# List tasks
curl "http://localhost:3101/tasks"
curl "http://localhost:3101/tasks?lane=Backlog&tag=urgent"
curl "http://localhost:3101/tasks?search=login"
# Create a task
curl -X POST http://localhost:3101/tasks \
-H 'Content-Type: application/json' \
-d '{"title":"My Task","content":"Details","lane":"Backlog","tags":["urgent"]}'
# Move a task
curl -X POST "http://localhost:3101/tasks/My%20Task/move" \
-H 'Content-Type: application/json' \
-d '{"lane":"In Progress"}'
# Statistics
curl http://localhost:3101/operations/statistics[tag:frontend]
[tag:urgent]
[due:2026-06-01]
Task body text goes here./data/tasks/
├── Backlog/
│ └── Implement login.md
├── In Progress/
│ └── Fix auth bug.md
├── Done/
│ └── Setup CI.md
└── Trash/
└── old-task.md
poetry run pytest --cov=task_lib --cov=task_api --cov=task_tui --cov-report=term-missing tests/TASK_CONFIG_PATH=config.yaml poetry run gunicorn --config task_api/gunicorn.conf.py "task_api.app:create_app()"TASK_CONFIG_PATH=config.yaml poetry run python mcp_task_service/server.pybin/tag-utility.py converts old-format tags (tags: tag1, tag2) to the current format ([tag:tagname]). Edit the hardcoded directory path in the script before running. Supports dry-run mode.