ATLAS: Task Management System

ATLAS (Adaptive Task & Logic Automation System) is a project, knowledge, and task management system for LLM Agents.

Built on a 3-node architecture:

                  +-------------------------------------------+
                  |                PROJECT                    |
                  |-------------------------------------------|
                  | id: string                                |
                  | name: string                              |
                  | description: string                       |
                  | status: string                            |
                  | urls?: Array<{title: string, url: string}>|
                  | completionRequirements: string            |
                  | outputFormat: string                      |
                  | taskType: string                          |
                  | createdAt: string                         |
                  | updatedAt: string                         |
                  +----------------+--------------------------+
                            |                    |
                            |                    |
                            v                    v
+----------------------------------+ +----------------------------------+
|               TASK               | |            KNOWLEDGE             |
|----------------------------------| |----------------------------------|
| id: string                       | | id: string                       |
| projectId: string                | | projectId: string                |
| title: string                    | | text: string                     |
| description: string              | | tags?: string[]                  |
| priority: string                 | | domain: string                   |
| status: string                   | | citations?: string[]             |
| assignedTo?: string              | | createdAt: string                |
| urls?: Array<{title: string,     | |                                  |
|   url: string}>                  | | updatedAt: string                |
| tags?: string[]                  | |                                  |
| completionRequirements: string   | |                                  |
| outputFormat: string             | |                                  |
| taskType: string                 | |                                  |
| createdAt: string                | |                                  |
| updatedAt: string                | |                                  |
+----------------------------------+ +----------------------------------+

Implemented as a Model Context Protocol (MCP) server, ATLAS allows LLM agents to interact with a project management database, enabling them to manage projects, tasks, and knowledge items.

Important Version Note: Version 1.5.4 is the last version that uses SQLite as the database. Version 2.0 and onwards has been completely rewritten to use Neo4j, which requires either:

• Self-hosting using Docker (docker-compose included in repository)
• Using Neo4j AuraDB cloud service: https://neo4j.com/product/auradb/

Version 2.5.0 introduces a new 3-node system (Projects, Tasks, Knowledge) that replaces the previous structure.

Table of Contents

• Overview
• Features
• Installation
• Running the Server
• Web UI (Experimental)
• Configuration
• Project Structure
• Tools
• Resources
• Database Backup and Restore
• Examples
• License

Overview

ATLAS implements the Model Context Protocol (MCP), enabling standardized communication between LLMs and external systems through:

• Clients: Claude Desktop, IDEs, and other MCP-compatible clients
• Servers: Tools and resources for project, task, and knowledge management
• LLM Agents: AI models that leverage the server's management capabilities

System Integration

The Atlas Platform integrates these components into a cohesive system:

• Project-Task Relationship: Projects contain tasks that represent actionable steps needed to achieve project goals. Tasks inherit context from their parent project while providing granular tracking of individual work items.
• Knowledge Integration: Both projects and tasks can be enriched with knowledge items, providing team members with necessary information and context.
• Dependency Management: Both projects and tasks support dependency relationships, allowing for complex workflows with prerequisites and sequential execution requirements.
• Unified Search: The platform provides cross-entity search capabilities, allowing users to find relevant projects, tasks, or knowledge based on various criteria.

Features

| Feature Area | Key Capabilities | | :----------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Project Management | - Comprehensive Tracking: Manage project metadata, statuses, and rich content (notes, links, etc.) with built-in support for bulk operations.<br />- Dependency & Relationship Handling: Automatically validate and track inter-project dependencies. | | Task Management | - Task Lifecycle Management: Create, track, and update tasks through their entire lifecycle.<br />- Prioritization & Categorization: Assign priority levels and categorize tasks with tags for better organization.<br />- Dependency Tracking: Establish task dependencies to create structured workflows. | | Knowledge Management | - Structured Knowledge Repository: Maintain a searchable repository of project-related information.<br />- Domain Categorization: Organize knowledge by domain and tags for easy retrieval.<br />- Citation Support: Track sources and references for knowledge items. | | Graph Database Integration | - Native Relationship Management: Leverage Neo4j's ACID-compliant transactions and optimized queries for robust data integrity.<br />- Advanced Search & Scalability: Perform property-based searches with fuzzy matching and wildcards while maintaining high performance. | | Unified Search | - Cross-Entity Search: Find relevant projects, tasks, or knowledge based on content, metadata, or relationships.<br />- Flexible Query Options: Support for case-insensitive, fuzzy, and advanced filtering options. |

Installation

1. Clone the repository:

   git clone https://github.com/cyanheads/atlas-mcp-server.git
   cd atlas-mcp-server
   

2. Install dependencies:

   npm install
   

3. Configure Neo4j: Ensure you have a Neo4j instance running and accessible. You can start one using the provided Docker configuration:

   docker-compose up -d
   

   Update your .env file with the Neo4j connection details (see Configuration).

4. Build the project:

   npm run build
   

Running the Server

Most MCP Clients run the server automatically, but you can also run it manually for testing or development purposes using the following commands.

ATLAS MCP Server supports multiple transport mechanisms for communication:

• Standard I/O (stdio): This is the default mode and is typically used for direct integration with local MCP clients (like IDE extensions).

  npm run start:stdio
  

  This uses the MCP_TRANSPORT_TYPE=stdio setting.

• Streamable HTTP: This mode allows the server to listen for MCP requests over HTTP, suitable for remote clients or web-based integrations.

  npm run start:http
  

  This uses the MCP_TRANSPORT_TYPE=http setting. The server will listen on the host and port defined in your .env file (e.g., MCP_HTTP_HOST and MCP_HTTP_PORT, defaulting to 127.0.0.1:3010). Ensure your firewall allows connections if accessing remotely.

Web UI (Experimental)

A basic Web UI is available for viewing Project, Task, & Knowledge details.

• Opening the UI:

  • To open the UI directly in your browser, run the following command in your terminal:

    npm run webui
    

• Functionality:

  • You can see an example screenshot of the Web UI here.

Configuration

Environment Variables

Environment variables should be set in the client config in your MCP Client, or in a .env file in the project root for local development.

# Neo4j Configuration
NEO4J_URI=bolt://localhost:7687
NEO4J_USER=neo4j
NEO4J_PASSWORD=password2

# Application Configuration
MCP_LOG_LEVEL=debug # Minimum logging level. Options: emerg, alert, crit, error, warning, notice, info, debug. Default: "debug".
LOGS_DIR=./logs # Directory for log files. Default: "./logs" in project root.
NODE_ENV=development # 'development' or 'production'. Default: "development".

# MCP Transport Configuration
MCP_TRANSPORT_TYPE=stdio # 'stdio' or 'http'. Default: "stdio".
MCP_HTTP_HOST=127.0.0.1 # Host for HTTP transport. Default: "127.0.0.1".
MCP_HTTP_PORT=3010 # Port for HTTP transport. Default: 3010.
# MCP_ALLOWED_ORIGINS=http://localhost:someport,https://your-client.com # Optional: Comma-separated list of allowed origins for HTTP CORS.

# MCP Security Configuration
# MCP_AUTH_SECRET_KEY=your_very_long_and_secure_secret_key_min_32_chars # Optional: Secret key (min 32 chars) for JWT authentication if HTTP transport is used. CRITICAL for production. *Note: Production environment use has not been tested yet.*
MCP_RATE_LIMIT_WINDOW_MS=60000 # Rate limit window in milliseconds. Default: 60000 (1 minute).
MCP_RATE_LIMIT_MAX_REQUESTS=100 # Max requests per window per IP for HTTP transport. Default: 100.

# Database Backup Configuration
BACKUP_MAX_COUNT=10 # Maximum number of backup sets to keep. Default: 10.
BACKUP_FILE_DIR=./atlas-backups # Directory where backup files will be stored (relative to project root). Default: "./atlas-backups".

Refer to src/config/index.ts for all available environment variables, their descriptions, and default values.

MCP Client Settings

How you configure your MCP client depends on the client itself and the chosen transport type. An mcp.json file in the project root can be used by some clients (like mcp-inspector) to define server configurations; update as needed.

For Stdio Transport (Example Configuration):