How to integrate Webflow MCP with Mastra AI

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Introduction

This guide walks you through connecting Webflow to Mastra AI using the Composio tool router. By the end, you'll have a working Webflow agent that can add a new blog post to my site, list all products in my store collection, get details for order #12345, delete a collection item by its id through natural language commands.

This guide will help you understand how to give your Mastra AI agent real control over a Webflow account through Composio's Webflow MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

TL;DR

Here's what you'll learn:
  • Set up your environment so Mastra, OpenAI, and Composio work together
  • Create a Tool Router session in Composio that exposes Webflow tools
  • Connect Mastra's MCP client to the Composio generated MCP URL
  • Fetch Webflow tool definitions and attach them as a toolset
  • Build a Mastra agent that can reason, call tools, and return structured results
  • Run an interactive CLI where you can chat with your Webflow agent

What is Mastra AI?

Mastra AI is a TypeScript framework for building AI agents with tool support. It provides a clean API for creating agents that can use external services through MCP.

Key features include:

  • MCP Client: Built-in support for Model Context Protocol servers
  • Toolsets: Organize tools into logical groups
  • Step Callbacks: Monitor and debug agent execution
  • OpenAI Integration: Works with OpenAI models via @ai-sdk/openai

What is the Webflow MCP server, and what's possible with it?

The Webflow MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Webflow account. It provides structured and secure access to your Webflow sites, collections, and e-commerce data, so your agent can perform actions like managing content, updating inventory, handling orders, and retrieving detailed site information on your behalf.

  • Effortless content management: Ask your agent to create, update, or delete collection items—perfect for adding new blog posts, products, or dynamic content without manual entry.
  • Comprehensive site and collection insights: Retrieve up-to-date details about your Webflow sites and collections, including schema, settings, and structure, to power content-aware automations.
  • Inventory and order automation: Have your agent check inventory levels, update stock, and mark orders as fulfilled, streamlining your Webflow e-commerce operations.
  • Bulk data handling: Let your agent list all items in a collection or all collections on a site, enabling smart reporting, audits, or content migrations with a simple prompt.
  • Seamless integration with creative workflows: Enable real-time, AI-driven updates to your site content, inventory, or orders in response to team or customer needs—no coding required.

Supported Tools & Triggers

Tools
Create Webflow Collection ItemThis tool creates a new item in a specified webflow collection.
Delete Webflow Collection ItemThis tool allows you to delete a specific item from a collection in webflow.
Fulfill OrderThis tool allows you to mark an order as fulfilled in webflow's e-commerce system.
Get Collection DetailsRetrieves a specific collection by its id from a webflow site.
Get Collection ItemThis tool retrieves a specific item from a webflow collection.
Get Item InventoryThis tool retrieves the current inventory levels for a specific item in a webflow collection.
Get Order DetailsThis tool retrieves detailed information about a specific order in webflow.
Get Webflow Site InformationThis tool retrieves detailed information about a specific webflow site.
List Collection ItemsThis tool retrieves a list of items from a specified collection in webflow.
List Webflow CollectionsThis tool retrieves a list of all collections for a given webflow site.
List Form SubmissionsThis tool retrieves a list of form submissions for a specific webflow site.
List Webflow OrdersThis tool retrieves a list of all orders for a specified webflow site using the get /sites/{site id}/orders endpoint.
List PagesThis tool retrieves a list of all pages for a specified webflow site.
List Webflow SitesThis tool retrieves a list of all webflow sites accessible to the authenticated user.
Publish Webflow SiteThis tool publishes a webflow site, making all staged changes live.
Refund OrderThis tool allows you to refund a webflow e-commerce order.
Unfulfill OrderThis tool allows you to mark a previously fulfilled order as unfulfilled in webflow.
Update Webflow Collection ItemThis tool allows updating an existing item in a webflow collection using the patch /collections/{collection id}/items/{item id} endpoint.
Update Item InventoryThis tool allows you to update the inventory levels of a specific sku item in your webflow e-commerce site by either setting the inventory quantity directly or updating it incrementally.
Update OrderThis tool allows updating specific fields of an existing order in webflow.
Upload Asset to WebflowThis tool allows users to upload assets (files, images, etc.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before starting, make sure you have:
  • Node.js 18 or higher
  • A Composio account with an active API key
  • An OpenAI API key
  • Basic familiarity with TypeScript

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key.
  • You need credits or a connected billing setup to use the models.
  • Store the key somewhere safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Go to Settings and copy your API key.
  • This key lets your Mastra agent talk to Composio and reach Webflow through MCP.

Install dependencies

bash
npm install @composio/core @mastra/core @mastra/mcp @ai-sdk/openai dotenv

Install the required packages.

What's happening:

  • @composio/core is the Composio SDK for creating MCP sessions
  • @mastra/core provides the Agent class
  • @mastra/mcp is Mastra's MCP client
  • @ai-sdk/openai is the model wrapper for OpenAI
  • dotenv loads environment variables from .env

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio
  • COMPOSIO_USER_ID tells Composio which user this session belongs to
  • OPENAI_API_KEY lets the Mastra agent call OpenAI models

Import libraries and validate environment

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey as string,
});
What's happening:
  • dotenv/config auto loads your .env so process.env.* is available
  • openai gives you a Mastra compatible model wrapper
  • Agent is the Mastra agent that will call tools and produce answers
  • MCPClient connects Mastra to your Composio MCP server
  • Composio is used to create a Tool Router session

Create a Tool Router session for Webflow

typescript
async function main() {
  const session = await composio.create(
    composioUserID as string,
    {
      toolkits: ["webflow"],
    },
  );

  const composioMCPUrl = session.mcp.url;
  console.log("Webflow MCP URL:", composioMCPUrl);
What's happening:
  • create spins up a short-lived MCP HTTP endpoint for this user
  • The toolkits array contains "webflow" for Webflow access
  • session.mcp.url is the MCP URL that Mastra's MCPClient will connect to

Configure Mastra MCP client and fetch tools

typescript
const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      nasdaq: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

console.log("Fetching MCP tools from Composio...");
const composioTools = await mcpClient.getTools();
console.log("Number of tools:", Object.keys(composioTools).length);
What's happening:
  • MCPClient takes an id for this client and a list of MCP servers
  • The headers property includes the x-api-key for authentication
  • getTools fetches the tool definitions exposed by the Webflow toolkit

Create the Mastra agent

typescript
const agent = new Agent({
    name: "webflow-mastra-agent",
    instructions: "You are an AI agent with Webflow tools via Composio.",
    model: "openai/gpt-5",
  });
What's happening:
  • Agent is the core Mastra agent
  • name is just an identifier for logging and debugging
  • instructions guide the agent to use tools instead of only answering in natural language
  • model uses openai("gpt-5") to configure the underlying LLM

Set up interactive chat interface

typescript
let messages: AiMessageType[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end.\n");

const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: "> ",
});

rl.prompt();

rl.on("line", async (userInput: string) => {
  const trimmedInput = userInput.trim();

  if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) {
    console.log("\nGoodbye!");
    rl.close();
    process.exit(0);
  }

  if (!trimmedInput) {
    rl.prompt();
    return;
  }

  messages.push({
    id: crypto.randomUUID(),
    role: "user",
    content: trimmedInput,
  });

  console.log("\nAgent is thinking...\n");

  try {
    const response = await agent.generate(messages, {
      toolsets: {
        webflow: composioTools,
      },
      maxSteps: 8,
    });

    const { text } = response;

    if (text && text.trim().length > 0) {
      console.log(`Agent: ${text}\n`);
        messages.push({
          id: crypto.randomUUID(),
          role: "assistant",
          content: text,
        });
      }
    } catch (error) {
      console.error("\nError:", error);
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    console.log("\nSession ended.");
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});
What's happening:
  • messages keeps the full conversation history in Mastra's expected format
  • agent.generate runs the agent with conversation history and Webflow toolsets
  • maxSteps limits how many tool calls the agent can take in a single run
  • onStepFinish is a hook that prints intermediate steps for debugging

Complete Code

Here's the complete code to get you started with Webflow and Mastra AI:

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({ apiKey: composioAPIKey as string });

async function main() {
  const session = await composio.create(composioUserID as string, {
    toolkits: ["webflow"],
  });

  const composioMCPUrl = session.mcp.url;

  const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      webflow: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

  const composioTools = await mcpClient.getTools();

  const agent = new Agent({
    name: "webflow-mastra-agent",
    instructions: "You are an AI agent with Webflow tools via Composio.",
    model: "openai/gpt-5",
  });

  let messages: AiMessageType[] = [];

  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: "> ",
  });

  rl.prompt();

  rl.on("line", async (input: string) => {
    const trimmed = input.trim();
    if (["exit", "quit"].includes(trimmed.toLowerCase())) {
      rl.close();
      return;
    }

    messages.push({ id: crypto.randomUUID(), role: "user", content: trimmed });

    const { text } = await agent.generate(messages, {
      toolsets: { webflow: composioTools },
      maxSteps: 8,
    });

    if (text) {
      console.log(`Agent: ${text}\n`);
      messages.push({ id: crypto.randomUUID(), role: "assistant", content: text });
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main();

Conclusion

You've built a Mastra AI agent that can interact with Webflow through Composio's Tool Router. You can extend this further by:
  • Adding other toolkits like Gmail, Slack, or GitHub
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows

How to build Webflow MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Webflow MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Webflow MCP with Claude Agent SDK

Google ADK

Google ADK

Use Webflow MCP with Google ADK

LangChain

LangChain

Use Webflow MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Webflow MCP with Vercel AI SDK

LlamaIndex

LlamaIndex

Use Webflow MCP with LlamaIndex

CrewAI

CrewAI

Use Webflow MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Webflow MCP?

With a standalone Webflow MCP server, the agents and LLMs can only access a fixed set of Webflow tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Webflow and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with Mastra AI?

Yes, you can. Mastra AI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Webflow tools.

Can I manage the permissions and scopes for Webflow while using Tool Router?

Yes, absolutely. You can configure which Webflow scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Webflow data and credentials are handled as safely as possible.

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