The Model Context Protocol (MCP) is the plug that lets an AI like Claude or ChatGPT read your data and take action in your tools, without a custom build for each tool. When we first published this article in March 2025, it was a young Anthropic project. In October 2026, it is a standard hosted by the Linux Foundation, supported by OpenAI, Google and Microsoft, and Webflow ships its own MCP server.
In this article, we explain what MCP is, how it works, where it stands today (specification, governance, supported tools), and what it actually changes for a team running a Webflow site.
Originally published in 2025, fully updated on October 6, 2026.
What is MCP in AI?
MCP is an open protocol that standardizes how an AI application connects to external data sources and tools. The official documentation compares it to a USB-C port for AI applications: one common plug, whatever the device.
On its own, a language model only knows what it was trained on and what you paste into the conversation. It cannot read your CRM or edit a page on your site. MCP gives it that access, within limits you set.
Anthropic open-sourced it on November 25, 2024, along with a first set of servers for Google Drive, Slack, GitHub, Git, Postgres and Puppeteer.
The problem it solves: M × N
Without a standard, connecting M AI applications to N tools takes M × N integrations: every assistant needs its own connector for every service. With MCP, each application implements the protocol once (a client), each service implements it once (a server), and you get M + N.
That is why it spread so fast: a vendor that publishes one MCP server becomes usable in Claude, ChatGPT, Cursor and VS Code at once.
How MCP works
Host, client, server
The specification defines three roles:
- the host, the AI application you use (Claude, ChatGPT, Cursor, VS Code);
- the client, the connector the host opens to each server;
- the server, the program that exposes a service (GitHub, a database, your CMS).
Messages use JSON-RPC 2.0. Two transports are standard: stdio, when the server runs on your machine, and Streamable HTTP, when it is hosted remotely. For remote servers, authorization relies on OAuth 2.1: you sign in to the service, accept the requested permissions, and the AI only gets that token.
The three building blocks of an MCP server
An MCP server can offer three kinds of things, which differ by who decides to use them (specification, Server Features):
| Building block | What it is | Who triggers it | Example |
|---|---|---|---|
| Tools | functions the AI can run | the model | create a CMS item, run a SQL query |
| Resources | data supplied as context | the application | a file's contents, Git history |
| Prompts | ready-made instruction templates | the user | an "SEO audit" command in a menu |
In practice, tools carry most of a server's value: they are what takes the AI from "here is what you should do" to "done".
What happens when you ask a question
- The application fetches the list of tools offered by the connected servers, with their descriptions.
- The model picks the right tool for your request and prepares the parameters.
- The application asks for your permission (the specification requires user consent before any tool call).
- The server runs the action and returns the result, which the model uses to answer or move to the next step.
In March 2025, we tried it in Claude Desktop with a community MCP server for Airbnb: you ask for a place to stay, Claude asks permission to use the tool, then answers with real listings.
MCP vs API: what's the difference?
An MCP server almost always sits on top of the API of the service it exposes. It adds a layer that every AI assistant knows how to read.
| Regular API | MCP server | |
|---|---|---|
| Built for | a developer writing code | an AI application |
| Discovery | you read the docs | the AI fetches the list of tools and their descriptions |
| Integration | one per client application | one, reused by every compatible client |
| Authorization | specific to each API | OAuth 2.1 in a shared pattern (remote servers) |
| Who decides to call | the code | the model, with the user's approval |
So if you already have an API, the decision is whether to also open it to AI assistants, not just to developers.
Where MCP stands in October 2026
The specification
The protocol is versioned by date. The published revisions are November 5, 2024, March 26, 2025, June 18, 2025, November 25, 2025 and July 28, 2026, the current one. According to its changelog, this latest version:
- makes the protocol stateless: no more initial handshake or HTTP session, every request carries its version and the client's capabilities;
- moves long-running tasks (Tasks) into an official extension, next to MCP Apps, which displays interfaces (forms, charts) right inside the conversation;
- deprecates three client-side features (Roots, Sampling, Logging), with a minimum twelve-month window before removal.
For a developer maintaining a server, these changes matter. For a marketing team using existing servers, they are invisible.
Governance
On December 9, 2025, Anthropic handed MCP over to the Agentic AI Foundation, a Linux Foundation fund co-founded by Anthropic, Block and OpenAI, with support from Google, Microsoft, AWS, Cloudflare and Bloomberg. The project is now a Linux Foundation entity, under the Apache 2.0 license.
Technical governance relies on maintainers appointed as individuals (no seat is reserved for any company), and every change goes through a public proposal, a SEP. The fragmentation risk we mentioned in 2025, with competing versions from different vendors, is much lower now.
Adoption
At the time of the handover, Anthropic counted more than 10,000 active public MCP servers and over 97 million monthly downloads of the Python and TypeScript SDKs. Since then, an official registry lists public servers; it is still in preview.
Which tools support MCP
On the AI application side (clients):
| Vendor | Where MCP is supported |
|---|---|
| Anthropic | Claude (web and desktop apps, Claude Code), through connectors |
| OpenAI | ChatGPT, with apps and developer mode: write actions in beta on Business, Enterprise and Edu, read-only on Pro. The Responses API also accepts remote MCP servers |
| Gemini CLI, with local and remote servers | |
| Microsoft | VS Code with GitHub Copilot (since version 1.102) and Copilot Studio |
| Cursor | the code editor, over stdio and HTTP |
On the service side (servers), things have changed since 2025. Most reference servers published by Anthropic (GitHub, Slack, Puppeteer, Postgres, Google Drive…) are now archived: vendors publish their own official servers instead. GitHub, for example, offers a hosted MCP server, generally available since September 2025. Our rule of thumb: look for the server published by the service's vendor first, before any community server.
Directories like Smithery still help you discover servers, but a community server deserves a careful look before you give it access.
Webflow's MCP server
For a team running a Webflow site, this is the most concrete use of MCP. Webflow publishes an official MCP server, built on its APIs, that lets an AI agent work directly in your projects.
What it can do
According to Webflow's documentation:
- manage the CMS: create collections, define fields, bulk-update items, publish or unpublish;
- manage pages, assets, forms and the sitemap, and update SEO metadata (title, description, Open Graph);
- build and edit the design: elements, classes, components, variables, GSAP interactions;
- read Webflow Analyze data (paid add-on): sessions, pageviews, top pages;
- run content audits: broken links, missing alt text.
How to connect it
Webflow offers a connector for Claude, an app in ChatGPT and a plugin for Cursor; for other tools (Claude Code, Windsurf…), you add the URL https://mcp.webflow.com/mcp (getting started guide). You then authorize access through OAuth. Each authorization covers a single workspace.
The MCP Bridge app installs automatically during authorization. The Designer only needs to be open for a few features: capturing a visual snapshot of an element, or reading and changing the current selection, page or breakpoint.
Built-in guardrails
The agent works within your Webflow roles and permissions, and nothing more. Its changes show up in the site's activity log. And each site can define Agent Instructions, site-specific rules (brand, design system, CMS conventions) the agent follows.
The documentation also lists limits: no Classic interactions, no creating CMS items in a secondary locale, no managing workspace access.
What we took away at Digidop
In our Webflow x Claude live session, Lucas Clairet and Florian Bodelot showed Claude at work on Webflow projects: SEO audits, CMS management, translation, page building. Our takeaway fits in one line: Claude is a copilot, not an autopilot. Results vary, the Webflow API has its limits, and a human review is still required before publishing.
For a step-by-step setup, we wrote a dedicated guide: how to connect Claude to your Webflow project.
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Limits and risks to know
- An MCP server acts with your permissions. An AI that can publish a page can also publish a bad one. Treat it like a contractor: minimal rights, review, an action log.
- The specification says so itself: MCP cannot enforce its security principles at the protocol level. Consent, permissions and clear display of actions depend on the application and the server.
- Tool descriptions provided by a server should be treated as untrusted, unless the server itself is trusted. A poorly built or malicious community server can steer the model.
- The more servers you connect, the more tools the model has to choose from, and the more likely it picks the wrong one. Connect the servers you need for the task at hand.
Our take
In March 2025, we presented MCP as a promise. In October 2026, whether it will win is settled: OpenAI, Google and Microsoft have adopted it, and its governance no longer depends on a single vendor.
For a marketing team, what's left is choosing which tasks to hand to an agent, and how much control to keep. On a Webflow site, repetitive and checkable tasks are good candidates: filling in SEO metadata, cleaning up a CMS collection, finding missing alt text, preparing a translation. Designing an offer page or publishing without review, much less so.
And if you sell a service or a tool, publishing an MCP server is becoming a visibility question, just like a documented API: without one, AI assistants can't use your product on your customers' behalf. It's the logical next step from what we describe in is your website ready for AI agents?.
FAQ
What is MCP in AI?
MCP (Model Context Protocol) is an open protocol that standardizes how an AI application connects to external tools and data: files, databases, CRMs, CMSs, APIs. A compatible application can use any MCP server without a custom integration.
What is an MCP server?
It is the program that exposes a service to the AI, as tools (actions), resources (data) and prompts (instruction templates). It can run on your computer or be hosted by the service vendor, like GitHub's or Webflow's servers.
What is the difference between MCP and an API?
MCP does not replace APIs: an MCP server usually sits on top of the service's API. It adds a common layer every AI assistant can read, with tool descriptions, parameters and authorization.
Which tools support MCP in 2026?
Claude, ChatGPT (apps and developer mode), OpenAI's Responses API, Gemini CLI, Visual Studio Code with GitHub Copilot, Microsoft Copilot Studio and Cursor, among others. On the service side, GitHub and Webflow each publish an official MCP server.
Is MCP secure?
The protocol defines OAuth 2.1-based authorization for remote servers and requires user consent before any tool call. But the specification states it cannot enforce these principles itself: security depends on the application and the server you choose.
Is MCP free and open source?
Yes. The specification and SDKs are released under the Apache 2.0 license and the project is hosted by the Linux Foundation. The services an MCP server connects to keep their own subscriptions.
Can you connect Webflow to Claude or ChatGPT with MCP?
Yes. Webflow publishes an official MCP server, with a connector for Claude, an app in ChatGPT and a Cursor plugin. You authorize a workspace through OAuth, then the AI can manage the CMS, pages, SEO and part of the design.
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