ATLAS - Automated Translation Layer for Agent Services

An intelligent MCP gateway that enables AI agents to interact with enterprise systems using natural language, without knowing the underlying APIs.

What is ATLAS?

ATLAS sits between AI agents (like Claude Code) and your backend systems (Jira, Wiki, Prometheus, etc.), translating natural language requests into the correct API calls.

┌─────────────────┐
│  Claude Code    │  "show me open JITP tickets"
│  or AI Agent    │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│     ATLAS       │  ← Understands intent, knows your systems
│   Gateway       │
└────────┬────────┘
         │
    ┌────┼────┬────────┐
    ▼    ▼    ▼        ▼
┌──────┐┌──────┐┌──────┐┌──────┐
│ Jira ││ Wiki ││Prom. ││ ...  │  ← MCP Servers
└──────┘└──────┘└──────┘└──────┘

Key benefits:

• Simple interface - 3 tools: query, action, health
• Intelligent routing - LLM-powered understanding of your request
• Domain knowledge - Knows your org's projects, timezones, conventions
• Trust signals - Every response includes confidence/completeness metadata

Quick Start

# Clone with submodules
git clone --recurse-submodules git@github.com:vilosource/atlas.git
cd atlas

# Configure
cp .env.example .env
# Edit .env with your API keys and credentials

# Start all services
make up

# Verify
make test

Services:

Service	URL	Description
ATLAS Dashboard	http://localhost:9991/dashboard/	Web UI for monitoring
ATLAS SSE	http://localhost:9991/sse	MCP endpoint for AI agents
mcp-atlassian	http://localhost:3001/sse	Jira/Confluence backend
mcp-mediawiki	http://localhost:3002/sse	Wiki backend
mcp-prometheus	http://localhost:3003/sse	Metrics backend

Using with Claude Code

# Add ATLAS as an MCP server
/mcp add atlas http://localhost:9991/sse

Then ask naturally:

• "show me open tickets in JITP"
• "search the wiki for deployment guides"
• "what's the CPU usage on production servers?"

Project Structure

atlas/
├── README.md                 # You are here
├── CLAUDE.md                 # AI assistant context
├── CHANGELOG.md              # Release history
├── docker-compose.yml        # Full stack orchestration
├── Makefile                  # Operations commands
├── .env.example              # Configuration template
│
├── docs/                     # Documentation
│   ├── INDEX.md              # Doc index
│   ├── architecture/         # Design specifications
│   └── guides/               # How-to guides
│       ├── getting-started-GUIDE.md
│       ├── development-GUIDE.md
│       └── deployment-GUIDE.md
│
├── kanban/                   # Project management
│   ├── backlog/              # Feature backlog by phase
│   └── planning/             # Roadmaps and sprint plans
│
├── atlas-core/               # Main ATLAS code (Git submodule)
│   ├── src/atlas/            # Source code
│   ├── tests/                # Test suite
│   └── Dockerfile            # Container build
│
├── mcp-atlassian/            # Jira/Confluence MCP (Git submodule)
├── mcp-mediawiki/            # MediaWiki MCP (Git submodule)
└── mcp-prometheus/           # Prometheus MCP (Git submodule)

Configuration

Copy .env.example to .env and configure:

# LLM (required)
ATLAS_OPENROUTER_API_KEY=sk-or-your-key

# Jira
JIRA_URL=https://your-org.atlassian.net
JIRA_USERNAME=your-email@example.com
JIRA_API_TOKEN=your-token

# MediaWiki
MW_API_HOST=wiki.example.com
MW_BOT_USER=Bot@atlas
MW_BOT_PASS=your-password

# Prometheus
PROMETHEUS_URL=https://prometheus.example.com

Common Commands

make help           # Show all commands
make up             # Start all services
make down           # Stop all services
make rebuild        # Rebuild and restart
make logs           # Follow all logs
make logs-atlas     # Follow ATLAS logs
make status         # Show container status
make test           # Health check all services
make shell-atlas    # Shell into ATLAS container

Architecture

ATLAS uses an LLM-first approach with data-driven optimization:

1. Learning Mode (current) - LLM handles all routing, logs decisions
2. Optimized Mode (future) - Pattern cache for fast paths, LLM fallback

Routing Components

Component	Purpose
MCPRouter	Central routing, domain classification, agent delegation
Domain Agents	Jira, Wiki, Prometheus - execute domain-specific queries
LLM Classifier	Understands intent via Claude Haiku
Decision Logger	Records decisions for pattern extraction
Memory	Stores user-defined rules
Pattern Cache	Fast path for known queries (future)

MCP Tools

Tool	Purpose
atlas.query(prompt)	Read operations
atlas.action(prompt)	Write operations
atlas.health()	System status

Documentation

• Getting Started - First-time setup
• Development Guide - Contributing
• Deployment Guide - Production setup
• Architecture - System design
• Backlog - Feature roadmap

Development Status

Phase	Description	Status
1	MCPRouter + Logging	COMPLETE
2	Memory Layer	Next
3	Multi-Backend Execution	Planned
4	Pattern Extraction	Planned
5	Model Optimization	Planned
6	Production Hardening	Planned

See kanban/INDEX.md for detailed roadmap.

Contributing

1. Check the backlog for open items
2. Read the development guide
3. Fork and submit PRs to the develop branch

License

MIT - See LICENSE for details.

Links

• GitHub: atlas - This workspace
• GitHub: atlas-core - Core code
• GitHub: mcp-atlassian
• GitHub: mcp-mediawiki
• GitHub: mcp-prometheus