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

Built on a three-tier 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 project management database, enabling managing 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.

• Overview
• Features
• Installation
• Configuration
• Project Structure
• Tools
• Resources
• Database Backup and Restore
• Examples
• Contributing
• License

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

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.

• Comprehensive Tracking: Manage project metadata, statuses, and rich content (notes, links, etc.) with built-in support for bulk operations.
• Dependency & Relationship Handling: Automatically validate and track inter-project dependencies.

• Task Lifecycle Management: Create, track, and update tasks through their entire lifecycle.
• Prioritization & Categorization: Assign priority levels and categorize tasks with tags for better organization.
• Dependency Tracking: Establish task dependencies to create structured workflows.

• Structured Knowledge Repository: Maintain a searchable repository of project-related information.
• Domain Categorization: Organize knowledge by domain and tags for easy retrieval.
• Citation Support: Track sources and references for knowledge items.

• Native Relationship Management: Leverage Neo4j's ACID-compliant transactions and optimized queries for robust data integrity.
• Advanced Search & Scalability: Perform property-based searches with fuzzy matching and wildcards while maintaining high performance.

• Cross-Entity Search: Find relevant projects, tasks, or knowledge based on content, metadata, or relationships.
• Flexible Query Options: Support for case-insensitive, fuzzy, and advanced filtering options.

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:

# Start Neo4j using Docker
docker-compose up -d

4. Build the project:

npm run build

Create a .env file based on .env.example:

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

# Application Configuration
LOG_LEVEL=info # debug, info, warn, error
NODE_ENV=development # development, production

Add to your MCP client settings:

{
  "mcpServers": {
    "atlas": {
      "command": "node",
      "args": ["/path/to/atlas-mcp-server/dist/index.js"],
      "env": {
        "NEO4J_URI": "bolt://localhost:7687",
        "NEO4J_USER": "neo4j",
        "NEO4J_PASSWORD": "password2",
        "LOG_LEVEL": "info",
        "NODE_ENV": "production"
      }
    }
  }
}

The codebase follows a modular structure:

src/
├── config/          # Configuration management (index.ts)
├── index.ts         # Main server entry point
├── mcp/             # MCP server implementation (server.ts)
│   ├── resources/   # MCP resource handlers (index.ts, types.ts, knowledge/, projects/, tasks/)
│   └── tools/       # MCP tool handlers (individual tool directories)
├── services/        # Core application services
│   └── neo4j/       # Neo4j database services (index.ts, driver.ts, backupRestoreService.ts, etc.)
├── types/           # Shared TypeScript type definitions (errors.ts, mcp.ts, tool.ts)
└── utils/           # Utility functions (logger.ts, errorHandler.ts, etc.)

Note: ID generation logic is primarily located in src/services/neo4j/helpers.ts.

ATLAS provides a comprehensive suite of tools for project, task, and knowledge management, callable via the Model Context Protocol.

ATLAS exposes project, task, and knowledge data through standard MCP resource endpoints.

ATLAS provides functionality to back up and restore the Neo4j database content. The core logic resides in src/services/neo4j/backupRestoreService.ts.

Important: The automatic backup functionality has been removed due to inefficiency. The call to triggerBackgroundBackup in src/services/neo4j/driver.ts is commented out with a note indicating it was removed. Please use the manual backup process described below to protect your data.

• Mechanism: The backup process exports all Project, Task, and Knowledge nodes, along with their relationships, into separate JSON files.
• Output: Each backup creates a timestamped directory (e.g., atlas-backup-YYYYMMDDHHMMSS) within the configured backup path (default: ./atlas-backups/). This directory contains projects.json, tasks.json, knowledge.json, and relationships.json.
• Manual Backup: You can trigger a manual backup using the provided script:

  npm run db:backup

  This command executes scripts/db-backup.ts, which calls the exportDatabase function.

• Mechanism: The restore process first completely clears the existing Neo4j database. Then, it imports nodes and relationships from the JSON files located in the specified backup directory.
• Warning: Restoring from a backup is a destructive operation. It will overwrite all current data in your Neo4j database.
• Manual Restore: To restore the database from a backup directory, use the import script:

  npm run db:import <path_to_backup_directory>

  Replace <path_to_backup_directory> with the actual path to the backup folder (e.g., ./atlas-backups/atlas-backup-20250326120000). This command executes scripts/db-import.ts, which calls the importDatabase function.
• Relationship Handling: The import process attempts to recreate relationships based on the id properties stored within the nodes during export. Ensure your nodes have consistent id properties for relationships to be restored correctly.

The examples/ directory contains practical examples demonstrating various features of the ATLAS MCP Server.

• Backup Example: Located in examples/backup-example/, this shows the structure and format of the JSON files generated by the npm run db:backup command. See the Examples README for more details.
• Deep Research Example: Located in examples/deep-research-example/, this demonstrates the output and structure generated by the atlas_deep_research tool. It includes a markdown file (covington_community_grant_research.md) summarizing the research plan and a JSON file (full-export.json) containing the raw data exported from the database after the research plan was created. See the Examples README for more details.

1. Fork the repository
2. Create a feature branch
3. Commit your changes with a descriptive message
4. Push to the branch
5. Create a Pull Request

For bugs and feature requests, please create an issue.

Apache License 2.0