CSA s

This MCP server collection exposes model context protocol endpoints for three Cloud Security Alliance chat models: ChatGPT, Claude, and Gemini. Each server acts as a distinct MCP server instance that can be queried using the Model Context Protocol to perform conversational tasks or retrieve context-aware responses. You can spin up each server independently and route MCP requests to the appropriate model, enabling modular testing and integration with your security-focused workflows. The included environment variables are placeholders for API keys and configuration options; replace them with your own credentials and desired log levels as needed.

Prerequisites:

• Node.js (version 14 or newer) installed on your system
• Access credentials for the respective model APIs (ChatGPT, Claude, Gemini)

Installation steps:

1. Prepare environment variables and directories for each server (adjust paths as needed):
   • Create directories for each server, e.g. chats/chat_chatgpt, chats/chat_claude, chats/chat_gemini
2. Install dependencies for each server (assuming a Node.js project structure):
   • cd path/to/chat_chatgpt && npm install
   • cd path/to/chat_claude && npm install
   • cd path/to/chat_gemini && npm install
3. Place your API keys and configuration into the environment or a .env file as appropriate for each server.
4. Start each server instance:
   • node path/to/chat_chatgpt/server.js
   • node path/to/chat_claude/server.js
   • node path/to/chat_gemini/server.js
5. Verify endpoints are reachable and MCP-compatible by issuing a basic Model Context Protocol request to each server’s port.

Notes:

• If you use a process manager (e.g., pm2, systemd), configure each server as its own process with the appropriate environment variables.
• Ensure network access to any external model APIs is permitted by your firewall.
• Adjust PORT and API key environment variables to your deployment environment.

Tips and considerations:

• Keep API keys secure and avoid embedding them directly in code; prefer environment variables or secret management.
• If a server fails to start, check the logs for missing environment variables or incorrect paths.
• You can rename the server entries (chat_chatgpt, chat_claude, chat_gemini) to reflect your internal naming conventions.
• If you switch to a different runtime (e.g., Python, Docker), adjust the mcp_config command and args accordingly and update the env block with any new variables required by that runtime.
• Document any model-specific rate limits or usage quotas to prevent unexpected throttling in production.
• Consider adding health check endpoints and standardized MCP query/response logging for easier observability.