The short version
MCP stands for Model Context Protocol. It's an open standard that lets AI assistants (like Claude or ChatGPT) plug directly into the tools and data they need to do useful work: your CRM, your spreadsheets, your inbox, or in our case, your Twain Agents.
In Twain, MCP means you can run your Twain Agent from inside another AI tool. Instead of switching tabs to Twain, you ask Claude (or any MCP-compatible client) to use your Twain Agent for you, and it does.
What is MCP, in plain English?
Think of MCP as the USB-C port for AI. Before USB-C, every device had its own cable. Before MCP, every AI integration was a one-off. Custom code to connect a model to Slack, a different setup for Google Drive, another for Salesforce.
MCP fixes that. It defines one standard way for an AI model to:
Read data from a tool (e.g., pull a list of leads from your CRM).
Take actions in a tool (e.g., draft an email, create a task, run a workflow).
Use templates and context the tool provides (e.g., the right prompt, the right examples).
Anthropic introduced MCP as an open standard in late 2024, and it's now supported by most major AI assistants and dev tools. Because it's open, any tool can build an "MCP server" and any AI model can be an "MCP client." That's why the ecosystem grew quickly.
What is MCP used for?
MCP is the layer that turns a chat model into something that can actually do things in your stack. Common uses:
Connecting AI to your work tools: Slack, Notion, Google Drive, GitHub, your CRM.
Letting AI run automations: kicking off a workflow, running a search agent, qualifying leads.
Giving AI live, current context: instead of relying only on what the model was trained on, it pulls fresh data on demand.
If you've used Claude Desktop with connectors, ChatGPT with connectors, or Cursor with custom integrations, you've already used MCP, even if you didn't see it.
What does MCP have to do with Twain?
Twain Agents can be deployed via MCP. That means your Twain Agent, with all its setup (buyer personas, signals, writing style, qualification rules), is reachable as a tool from any MCP-compatible AI client.
In practice:
You're working in Claude reviewing an account list. You ask Claude to run your Twain outbound agent on the top 20 leads. It does.
You're in your own internal AI tool and want it to use Twain's qualification logic before passing leads to a rep. The tool calls your Twain Agent over MCP.
Your ops team is building an automation and wants Twain in the loop alongside Clay, your CRM, and your inbox, without writing custom integration code for each piece.
The agent stays the same. You don't rebuild it for MCP. The same agent you use in a Twain campaign is the one that gets exposed via MCP, with the same personas, signals, and style.
When should you use Twain via MCP vs. directly?
Use Twain directly when you're running standard outbound, inbound, or nurture campaigns. The Twain UI is built for this.
Use Twain via MCP when you want to call your agent from somewhere else: an AI assistant, a custom workflow, or a tool that orchestrates several systems together.
Most customers use both: Twain directly for campaign-level work, and MCP when they want their agent to plug into a broader AI workflow.
Getting started
To deploy a Twain Agent via MCP, you'll need an existing agent set up in Twain (see Getting Started with Twain Agents) and an MCP-compatible client on the other end (Claude Desktop, Cursor, your own app, etc.).
If you'd like help wiring up Twain over MCP for your specific setup, reach out to us in the chat, and we'll walk through it with you.