Model Context Protocol (MCP) is a standard protocol introduced by Anthropic that defines how large language models can interface with systems in a structured, predictable way. Instead of each AI tool creating its own custom integration method, MCP provides a common framework that works across different platforms and applications.
Why MCP matters for AI integrations
Before MCP, connecting AI tools to services meant building custom integrations for each combination of AI platform and service. If you wanted your AI assistant to access five different tools, you'd need five different integration approaches. MCP changed this by creating a universal standard.
Here's what this means in practice. Let's say your finance team uses an AI assistant to help with invoice processing. With MCP, that assistant can:
Query your accounting system for invoice data.
Trigger a Tines workflow to route invoices for approval.
Update your Enterprise Resource Planning (ERP) system with payment information.
All through a single, standardized protocol.
The beauty of MCP is that once you build an MCP server in Tines, any MCP-compatible AI client can use it. You're not locked into a single AI platform or vendor.
MCP architecture and components
An MCP setup has three main parts:
MCP clients: These are the AI applications that want to use external tools. Examples include Claude Desktop, Cursor, VSCode with AI extensions, or custom AI applications your team builds.
MCP servers: These expose tools and capabilities that AI clients can use. In Tines, you'll build MCP servers that make your workflows and integrations available to AI clients.
Protocol (the "P" in MCP): This is the standardized communication layer that lets clients and servers talk to each other, regardless of who built them.
When an AI client connects to your Tines MCP server, it can discover what tools are available, understand how to use them, and invoke them as needed. The protocol handles all the technical details of that communication.