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Project Manager MCP with SQLite database layer and atomic locking

Project description

Project Manager MCP

PyPI version Python 3.9+ License: MIT Code style: black Tests Coverage

Quick Start

  1. Clone this repo
git clone https://github.com/Commands-com/pm.git
cd pm
  1. Install Claude assets (for Claude users)
uvx --from /path/to/pm project-manager-mcp install-claude-assets --target-dir ~/my-project
  1. Add to your AI assistant:

Claude

claude mcp add project-manager -- uvx --from /path/to/pm project-manager-mcp

Codex

[mcp_servers.project-manager-mcp]
command = "uvx"
args = ["--from", "/path/to/pm", "project-manager-mcp"]

Gemini

"mcpServers": {
  "project-manager": {
    "type": "stdio",
    "command": "uvx",
    "args": [
      "--from",
      "/path/to/pm",
      "project-manager-mcp"
    ],
    "env": {}
  }
}

Features

A comprehensive project management system with Model Context Protocol (MCP) support, enabling AI agents to manage projects, epics, and tasks through both programmatic interfaces and a web dashboard.

  • AI Agent Integration: MCP tools for autonomous project management
  • Web Dashboard: Real-time web interface for project visualization
  • Task Locking System: Atomic operations prevent concurrent modifications
  • WebSocket Updates: Real-time synchronization across all clients
  • SQLite Backend: Lightweight, serverless database with WAL mode
  • Zero-Config Setup: Single command deployment with automatic port allocation
  • Project Import: YAML-based project definition and import system
  • RA Tag Context Detection: Zero-effort context capture for Response Awareness tags

Installation & Usage

Installation Options

# Install from source (development)
pip install -e .

# Or install development dependencies
pip install -e .[dev]

# Run directly with uvx (no installation needed)
uvx --from . project-manager-mcp

Basic Usage

# Start with default configuration (dashboard on :8080, MCP over stdio)
project-manager-mcp

# Custom port and options
project-manager-mcp --port 9000 --no-browser

# Import a project on startup
project-manager-mcp --project examples/simple-project.yaml

# MCP over stdio (default; for shell integration)
project-manager-mcp --mcp-transport stdio

# Add RA tags with automatic context detection
python -m task_manager.cli add-ra-tag "#COMPLETION_DRIVE_IMPL: Assuming user validation upstream" --task-id 123

# Install Claude agents and commands to your project
project-manager-mcp install-claude-assets --target-dir ~/my-project

After startup, access the dashboard at http://localhost:8080 (or your chosen port).

MCP Client Integration

Connect MCP clients to interact programmatically:

# Stdio transport (default)
# Connect via stdin/stdout

# SSE transport (optional)
project-manager-mcp --mcp-transport sse
# Connect to http://localhost:8081/sse

# Using uvx for MCP integration
uvx --from . project-manager-mcp --mcp-transport stdio
uvx --from . project-manager-mcp --mcp-transport sse --port 9000

Architecture Overview

Core Components

  • CLI Interface (task_manager.cli): Zero-config server coordination
  • FastAPI Backend (task_manager.api): REST endpoints and WebSocket broadcasting
  • MCP Server (task_manager.mcp_server): AI agent tool integration
  • Database Layer (task_manager.database): SQLite with atomic locking
  • MCP Tools (task_manager.tools): GetAvailableTasks, AcquireTaskLock, UpdateTaskStatus, ReleaseTaskLock, AddRATag
  • Context Detection (task_manager.context_utils): Automatic file, git, and symbol context detection

Data Model

Projects (top-level containers)
├── Epics (high-level initiatives)
    ├── Tasks (specific work items)

Each task supports:

  • Status tracking: pending → in_progress → completed
  • Atomic locking: Prevent concurrent modifications
  • Agent assignment: Track work ownership
  • Real-time updates: WebSocket broadcasting
  • RA Tag Context: Automatic context detection for Response Awareness tags

Transport Modes

  1. SSE (Server-Sent Events): HTTP-based MCP for network clients
  2. Stdio: Pipe-based MCP for shell and local integration
  3. None: Dashboard-only mode without MCP server

Key Features

Atomic Task Locking

Two patterns are supported:

  1. Single-call update (auto-lock):
# Automatically acquires a short-lived lock if unlocked, updates status, then releases.
mcp_client.call_tool("update_task_status", {
    "task_id": "123",
    "status": "DONE",            # UI vocabulary also accepted
    "agent_id": "agent-1"
})
  1. Explicit lock + update (long-running work):
# Acquire exclusive lock on task (status moves to IN_PROGRESS)
mcp_client.call_tool("acquire_task_lock", {
    "task_id": "123",
    "agent_id": "agent-1",
    "timeout": 300
})

# Perform work...

# Update status and auto-release on DONE
mcp_client.call_tool("update_task_status", {
    "task_id": "123",
    "status": "DONE",
    "agent_id": "agent-1"
})

Real-time Dashboard Updates

WebSocket events keep all clients synchronized:

  • task.status_changed - Task status updates
  • task.locked - Task lock acquisition
  • task.unlocked - Task lock release

Project Import System

Define projects in YAML and import on startup:

projects:
  - name: "User Management System"
    description: "Complete user lifecycle management"
    epics:
      - name: "User Authentication"
        status: "ACTIVE"
        tasks:
          - name: "Create registration form"
            status: "TODO"
          - name: "Implement login validation"
            status: "TODO"

Use Cases

AI Agent Workflows

  1. Query available work: get_available_tasks
  2. Claim exclusive access: acquire_task_lock
  3. Update progress: update_task_status
  4. Release when done: Auto-release on completion

Multi-Agent Coordination

  • Prevent conflicts: Atomic locking prevents multiple agents on same task
  • Work distribution: Available task querying enables load balancing
  • Progress tracking: Status updates provide visibility across agents
  • Real-time sync: WebSocket updates keep all systems current

Dashboard Management

  • Project visualization: Project → Epic → Task hierarchy
  • Real-time monitoring: Live updates from agent activity
  • Manual intervention: Override task states when needed
  • Project import: Load new projects without restart

Configuration

CLI Options

  • --port PORT: Dashboard server port (default: 8080)
  • --mcp-transport {stdio|sse|none}: MCP transport mode (default: stdio)
  • --project PATH: Import project YAML on startup
  • --no-browser: Skip automatic browser launch
  • --host HOST: Server bind address (default: 127.0.0.1)
  • --db-path PATH: Database file location (default: project_manager.db)
  • --verbose: Enable debug logging

Claude Assets Installation

Install Claude Code agents and commands to your projects:

# Install both agents and commands to a project
project-manager-mcp install-claude-assets --target-dir ~/my-project

# Install with overwrite protection
project-manager-mcp install-claude-assets --target-dir ~/my-project --force

# Install only agents
project-manager-mcp install-claude-assets --target-dir ~/my-project --agents-only

# Install only commands
project-manager-mcp install-claude-assets --target-dir ~/my-project --commands-only

# Verbose output showing all installed files
project-manager-mcp install-claude-assets --target-dir ~/my-project --verbose

# Alternative standalone command
pm-install-claude-assets --target-dir ~/my-project

This creates a .claude/ directory in your target location with:

  • Agents (.claude/agents/): Specialized agents for adaptive assessment, planning review, task execution, and verification
  • Commands (.claude/commands/pm/): Project management commands for task workflow, epic management, and status tracking

Environment Variables

  • DATABASE_PATH: Override default database location
  • DEBUG: Enable verbose logging

Performance Characteristics

  • Startup time: < 2 seconds with empty database
  • Task operations: < 50ms for lock acquisition/release
  • WebSocket latency: < 10ms for local connections
  • Concurrent agents: Tested with 50+ simultaneous agents
  • Database size: Handles 10,000+ tasks efficiently

Error Recovery

  • Port conflicts: Automatic alternative port allocation
  • Database corruption: WAL mode provides crash recovery
  • WebSocket disconnections: Automatic reconnection handling
  • Lock timeouts: Automatic cleanup of expired locks
  • Agent failures: Lock expiration prevents indefinite blocking

Security Model

  • No authentication: Open access for development and testing
  • Local binding: Default 127.0.0.1 limits network exposure
  • File permissions: Database protected by filesystem ACLs
  • Input validation: Pydantic models prevent injection attacks
  • Resource limits: Lock timeouts prevent resource exhaustion

Troubleshooting

Common Issues

Port already in use

# Use alternative ports
project-manager-mcp --port 9000

# Check what's using the port
lsof -i :8080

Database locked errors

# Check for competing processes
ps aux | grep project-manager-mcp

# Remove database if corrupted
rm project_manager.db

WebSocket connection refused

# Verify server is running
curl http://localhost:8080/healthz

# Check WebSocket endpoint
curl -H "Upgrade: websocket" http://localhost:8080/ws/updates

MCP client connection issues

# Test SSE endpoint (when using --mcp-transport sse)
curl http://localhost:8081/sse

# For stdio mode, verify no conflicting processes
project-manager-mcp --mcp-transport stdio --verbose

Documentation

Examples

License

This project is licensed under the MIT License - see the LICENSE file for details.

You are free to use, modify, and distribute this software for any purpose, including commercial use.

Contributing

We welcome contributions! Please see CONTRIBUTING.md for:

  • Development setup instructions
  • Code style guidelines
  • Testing requirements
  • Pull request process
  • Response Awareness (RA) methodology guidelines

For detailed architecture and development information, see Development Guide.

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