Connect any tool to your AI via MCP
Model Context Protocol is the emerging standard for connecting data sources and tools to AI agents. Instead of writing custom integration code for every tool you want your agent to use, MCP gives you one protocol that works across everything. Any data source. Any API. Any database. One standard interface.
This guide is built on running MuninnDB via MCP in production with 30+ tools connected. That production context means every configuration example, every troubleshooting guide, and every security consideration comes from real-world usage — not documentation examples. When something breaks, we know what the failure mode looks like because we've seen it.
The guide covers the full MCP stack: protocol overview, server configuration, tool discovery, multi-server routing, and security. Five working server configurations are included — MuninnDB (the memory database that powers our production system), Notion, Postgres, a generic REST API template, and a custom Python server template you can adapt to any data source. Each configuration includes the exact JSON for OpenClaw's MCP config block.
Multi-server routing is one of the most powerful features of MCP and one of the least documented. When you have 30+ tools across multiple servers, the router needs to know which server to query for which tool. This guide covers the routing configuration patterns that make that work correctly, including how to handle tool name collisions across servers.
Security considerations include credential management, server-level authentication, rate limiting, and scope restriction. Running MCP servers exposes a significant surface area if not configured correctly. The guide walks through the patterns we use to keep our production servers locked down while remaining accessible to the agent.
MCP is the trajectory of agent tooling. Getting ahead of it now means your stack will compose cleanly as the ecosystem grows. This guide is the fastest path from zero to a working multi-server MCP environment.