MCP Servers

A collection of Model Context Protocol servers, templates, tools and more.

Enterprise MCP server connecting Claude, ChatGPT & AI assistants to databases, APIs & business systems | Docker-ready with LibreChat UI | Transform any system into AI tools in minutes

Created 9/2/2025
Updated 5 days ago
Repository documentation and setup instructions

Catalyst MCP Server

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

Docker MCP Protocol Pack Builder License: MIT GitHub Stars

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:

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: Chat Customization Guide

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

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

  1. Clone repository: git clone https://github.com/billebel/catalyst_mcp.git
  2. Install pack builder: pip install catalyst-builder
  3. Create packs as needed
  4. Deploy with Docker: docker-compose up -d
Quick Setup
Installation guide for this server

Install Package (if required)

uvx catalyst_mcp

Cursor configuration (mcp.json)

{ "mcpServers": { "billebel-catalyst-mcp": { "command": "uvx", "args": [ "catalyst_mcp" ] } } }