catalyst_mcp by billebel - MCP Server

Catalyst MCP Server

MCP (Model Context Protocol) server implementation that loads and serves Knowledge Packs.

Features

MCP server implementation with FastAPI
LibreChat integration for web interface
Docker deployment support
Knowledge Pack loading from YAML configurations
Authentication and rate limiting
Support for multiple AI models (Claude, GPT, Gemini)

Quick Start

1. Clone and Configure

# Clone the repository
git clone https://github.com/billebel/catalyst_mcp.git
cd catalyst_mcp

# Copy environment template
cp .env.example .env

# Edit .env with your API keys
nano .env

2. Set Your API Keys

Edit .env file:

# Add your API keys
ANTHROPIC_API_KEY=your-claude-api-key
OPENAI_API_KEY=your-openai-api-key        # Optional
GOOGLE_API_KEY=your-gemini-api-key        # Optional

# JWT secrets (change for production!)
JWT_SECRET=your-secure-jwt-secret
JWT_REFRESH_SECRET=your-secure-refresh-secret

3. Start with Docker

# Start the complete stack
docker-compose up -d

# View logs
docker-compose logs -f

4. Access Your AI Assistant

Web Chat Interface: http://localhost:3080
MCP Server: http://localhost:8443
API Documentation: http://localhost:8443/docs

Architecture

graph TD
    A[AI Assistant<br/>Claude Desktop] --> B[MCP Protocol]
    C[Web Chat<br/>LibreChat] --> B
    B --> D[Catalyst MCP Server]
    D --> E[Knowledge Packs]
    E --> F[Your Business Systems]
    F --> G[Databases]
    F --> H[REST APIs] 
    F --> I[Cloud Services]

Knowledge Packs

Catalyst includes example Knowledge Packs for common business systems:

Pack	Description	Use Cases
PostgreSQL Analytics	Database queries and reporting	Business intelligence, data analysis
GitHub DevOps	Repository management and CI/CD	Code management, deployment tracking
GitLab DevOps	GitLab API integration	Project management, pipeline monitoring
Linux Server Admin	Server management and monitoring	System administration, log analysis
RabbitMQ Messaging	Message queue management	Queue monitoring, message handling
S3 Storage	AWS S3 file operations	File management, backup operations

Creating Custom Packs

Create Knowledge Packs using the Catalyst Builder:

# Install the pack builder
pip install catalyst-builder

# Create a new CRM integration pack
catalyst-packs create crm-integration \
  --type rest \
  --description "Connect to our CRM system"

# This generates a complete pack structure:
# crm-integration/
# ├── pack.yaml           # Main configuration
# ├── tools/              # Tool definitions
# ├── prompts/            # AI prompts
# └── README.md           # Documentation

The generated pack.yaml:

metadata:
  name: crm-integration
  description: "Connect to our CRM system"
  domain: sales

connection:
  type: rest
  base_url: "https://api.yourcrm.com/v1"
  auth:
    method: bearer
    token: "${CRM_API_TOKEN}"

tools:
  - name: search_customers
    type: search
    description: "Find customers by name or email"
    endpoint: "/customers/search"

Pack Builder Resources:

Catalyst Builder GitHub - Complete documentation
Getting Started Guide - Step-by-step tutorial
CLI Reference - All available commands
Pack Development Guide - Advanced configuration

Deployment Options

Docker Compose (Recommended)

# Production deployment
docker-compose up -d

# Development with hot reload
docker-compose -f docker-compose.yml -f docker-compose.override.yml up -d

Local Development

# Install Python dependencies
pip install -r requirements.txt

# Start MCP server
python -m catalyst_mcp.server

# Start chat interface (separate terminal)
# See docs/chat-customization.md for LibreChat setup

Configuration

Environment Variables

Variable	Description	Required	Default
`MCP_PORT`	MCP server port	No	`8443`
`MCP_HOST`	Server bind address	No	`0.0.0.0`
`LOG_LEVEL`	Logging level	No	`INFO`
`ANTHROPIC_API_KEY`	Claude API key	Yes*	-
`OPENAI_API_KEY`	OpenAI API key	No	-
`GOOGLE_API_KEY`	Gemini API key	No	-
`JWT_SECRET`	Chat authentication	Yes	-
`ALLOW_REGISTRATION`	Allow new users	No	`false`

*At least one AI provider API key is required.

Chat Interface Customization

Catalyst uses LibreChat for the web interface. Customize:

Branding: Edit librechat.yaml for colors, logos
Authentication: Configure OAuth providers in .env
Models: Enable/disable AI models per user
Plugins: Add custom plugins and tools

See:

AI Assistant Integration

Claude Desktop

Add to your Claude Desktop configuration:

{
  "mcpServers": {
    "catalyst": {
      "command": "mcp-client",
      "args": ["--url", "http://localhost:8443"]
    }
  }
}

ChatGPT/OpenAI

Use the MCP-compatible plugin or direct API integration.

Custom AI Applications

Connect any MCP-compatible AI application:

import mcp_client

# Connect to Catalyst MCP server
client = mcp_client.MCPClient("http://localhost:8443")

# Use business tools
result = client.call_tool("search_customers", {"query": "ACME Corp"})

Security Features

Authentication & Authorization

JWT-based session management
Role-based access control
OAuth provider integration (GitHub, Google, etc.)

API Security

Rate limiting and request throttling
Input validation and sanitization
Audit logging for compliance

Deployment Security

HTTPS/TLS encryption
Environment variable secrets
Container isolation

Examples & Use Cases

Business Intelligence

Use the Catalyst Builder to create database analytics packs:

catalyst-packs create bi-dashboard --type database --description "Executive dashboard"

DevOps Automation

Create deployment and monitoring packs:

catalyst-packs create devops-tools --type rest --description "CI/CD automation"

Customer Support

Build support system integrations:

catalyst-packs create support-tools --type rest --description "Help desk integration"

Community & Support

Catalyst Builder: catalyst-builder on PyPI
Documentation: Complete Pack Development Guide
GitHub Issues: Report bugs and request features

License

MIT License

Quick Commands

# Start everything
docker-compose up -d

# View logs
docker-compose logs -f catalyst-mcp

# Stop services
docker-compose down

# Create custom packs
pip install catalyst-builder
catalyst-packs create my-integration --type rest

Getting Started

Clone repository: git clone https://github.com/billebel/catalyst_mcp.git
Install pack builder: pip install catalyst-builder
Create packs as needed
Deploy with Docker: docker-compose up -d