ScarletMesh

ScarletMesh is a monorepo for intelligent MCP (Model Context Protocol) services that provide contextual Red Hat data, weather information, and Ansible automation through natural language queries. Chat naturally with AI that can automatically access specialized tools and data sources.

🚀 Quick Start - Python Client (Recommended)

The Python Ollama Client is ready to use with all tools working!

# 1. Install Ollama and pull a model
curl -fsSL https://ollama.com/install.sh | sh
ollama pull llama3.2:3b

# 2. Navigate to Python client
cd mcp-client/python-client

# 3. Install and run
poetry install
poetry run ollama-mcp run ../../packages/

✅ Verified Working:

• 🦙 8 Ollama models detected and switchable
• 🛠️ 14 MCP tools loaded (Red Hat, Weather, Ansible, CVE)
• 🔧 Tool execution confirmed with real data
• 💬 Rich terminal UI with colors and tables

📖 Python Client Documentation →

🧠 Overview

ScarletMesh enables developers, Red Hat teams, and security professionals to access information through natural language conversations. The AI automatically selects and uses the right tools based on your questions.

Available MCP Services

• 🔍 Red Hat Labs Finder - Find tools, guides, and configurations
• 🛡️ CVE Insight Server - Security vulnerability data and analysis
• 📆 Product Lifecycle Server - Red Hat product EOL and support info
• 🌤️ Weather Service - Weather alerts, forecasts, and conditions
• 🤖 Ansible Info - Version info, modules, and playbook generation

Client Options

| Client | Status | Best For | Setup | |--------|--------|----------|-------| | 🦙 Python Ollama Client | ✅ Fully Working | Production use, model switching | 4 simple steps | | 🔧 TypeScript Client | Development | Web integration, Ollama testing | More complex |

→ Use the Python client for the best experience!

🛠️ Development Setup

Prerequisites

• Node.js v22.14.0 (for MCP servers)
• Python 3.8+ (for Python client)
• Ollama (for AI models)

Build All MCP Servers

# Install dependencies
npm install

# Build all MCP servers at once
npx nx run @scarlet-mesh:build-all

# Verify servers were built
ls packages/*/build/index.js

Alternative Development Commands

# Build individual service
npx nx build <project-name>

# Run TypeScript client (development)
npx nx run client-setup:build

# Run frontend
npx nx run frontend:dev

# View workspace graph
npx nx graph

💬 Example Conversations

Red Hat Resources:

You: Find Red Hat labs about container security
🦙 AI: I'll search Red Hat resources for container security tools.
🔧 Calling find-lab...
📊 Found: Container security guides, tools, and best practices

Weather Information:

You: What's the weather like in California?
🦙 AI: I'll check weather conditions for California.
🔧 Calling get-alerts...
📊 Current alerts and conditions for CA

Security Analysis:

You: Tell me about CVE-2024-12345
🦙 AI: I'll analyze that CVE for you.
🔧 Calling summarize-cve...
📊 Vulnerability details, severity, and recommendations

Ansible Automation:

You: What Ansible modules are available?
🦙 AI: I'll get the list of available Ansible modules.
🔧 Calling get-ansible-modules...
📊 Complete list: command, shell, copy, file, yum, apt, etc.

📐 Architecture

MCP Services (14 Tools Total)

• Red Hat Tools (5): Labs finder, product info, lifecycle data
• Security Tools (2): CVE analysis and summaries
• Weather Tools (3): Alerts, forecasts, sunrise/sunset
• Ansible Tools (4): Version info, modules, guides, playbook generation

Clients

• Python Ollama Client: Production-ready with rich terminal UI
• TypeScript Client: Development/web integration focused

Data Flow

1. User asks natural language question
2. AI determines which tools to use
3. Tools fetch real-time data from APIs
4. AI analyzes and presents results

🧪 Testing & Verification

All Python client functionality is thoroughly tested:

cd mcp-client/python-client

# ✅ Test basic functionality (4/4 tests pass)
poetry run python quick_test.py

# ✅ Test Ollama connection (8 models detected)  
poetry run ollama-mcp test

# ✅ Test tool loading (14 tools confirmed)
poetry run python test_client_startup.py

# ✅ Test tool execution (verified working)
poetry run python test_tool_execution.py

🚀 Deployment Status

Ready for Production ✅

• Python Ollama Client: Fully tested and working
• MCP Services: 5 servers built and operational
• Tool Integration: 14 tools verified functional
• Model Support: 8 Ollama models available

Development Phase 🔧

• TypeScript client improvements
• Additional MCP service integrations
• Web frontend enhancements

🤝 Stakeholders

• 🔧 Engineering Teams - Ansible automation and tooling
• 💬 Customer Support - Product lifecycle and documentation
• 🧑‍💼 Product Management - Red Hat product information
• 🧪 Security Response Team - CVE analysis and vulnerability data
• 🧰 Red Hat Labs Maintainers - Tool and guide discovery
• 🌦️ Operations Teams - Weather-aware infrastructure decisions

📚 Documentation

• 📖 Python Client Guide - Complete setup and usage
• 🔧 Technical Planning Document
• 📐 Nx Documentation - Build system reference
• 🦙 Ollama Documentation - AI model information

💡 Quick Tips

For Users

# Start chatting immediately
cd mcp-client/python-client && poetry run ollama-mcp run ../../packages/

# Switch models during conversation  
You: model qwen3:14b

# List all available tools
You: tools

For Developers

# Build all services
npx nx run @scarlet-mesh:build-all

# Run tasks
npx nx <target> <project>

# Generate new libraries
npx nx g @nx/js:lib packages/<pkg-name> --publishable --importPath=@scarletmesh/<pkg-name>

🎯 Success Metrics

• ✅ 14 tools successfully integrated and tested
• ✅ 8 AI models available with runtime switching
• ✅ 5 MCP servers built and operational
• ✅ 4/4 tests passing in comprehensive test suite
• ✅ Real-time data from live APIs (Ansible, weather, CVE feeds)

ScarletMesh delivers production-ready AI assistance with specialized tools! 🚀