MCP Integration Guide: Connecting AI Agents to Enterprise Tools

The Model Context Protocol (MCP) has become the standard for connecting AI agents to external tools. This guide walks through integrating MCP servers into agentic workflows on the Yanok platform.

What is MCP?

MCP is a protocol that lets AI models interact with external tools through a standardized interface. Instead of building custom integrations for each tool, you deploy an MCP server that exposes the tool's capabilities through a consistent API.

Architecture Overview

An MCP integration has three layers:

• Transport: stdio (local) or HTTP/SSE (remote)
• Protocol: JSON-RPC messages for tool discovery and invocation
• Server: Your tool adapter implementing the MCP specification

Setting Up Your First MCP Server

Let's connect a PostgreSQL database to an agentic workflow:

// Install the official server
npx @anthropic/mcp-postgres

// Configure in Yanok
{
  "server": "@anthropic/mcp-postgres",
  "transport": "stdio",
  "env": { "DATABASE_URL": "postgresql://..." }
}

Available Servers on Yanok

Our MCP Registry includes 30+ verified servers covering: CRM (Salesforce, HubSpot), Communication (Slack, Discord), Development (GitHub, Linear), Databases (PostgreSQL, Supabase), and more.

Authentication Handling

Yanok manages OAuth flows for MCP servers automatically. When a workflow needs access to a user's Salesforce data, the platform handles token refresh and scope management transparently.

Best Practices

• Use rate limiting to prevent API abuse from autonomous agents
• Enable full tracing for audit compliance
• Set up fallback servers for critical integrations
• Test with read-only permissions before enabling writes