Tara Agyemang (Google Chrome team) introduces WebMCP, a proposed web standard that lets AI agents interact with sites via structured, named, typed tools instead of parsing the DOM, accessibility tree, or screenshots. The talk covers two implementation paths: a declarative API using HTML attributes on existing forms (browser auto-generates the JSON schema) and an imperative API registering custom JavaScript tools for multi-step flows. Includes a live demo completing a concert ticket purchase in three tool calls. Noted as experimental and in early preview on Chrome 146, with an eval CLI and inspector extension available for testing. # Channel Presentation Observations: WebMCP Talk ## Explicit Self-Description Evidence ### Speaker and Team Affiliation - The speaker introduces herself directly: *"my name is Tara. I am part of the Google Chrome team... I'm a developer relations engineer."* - She states she is presenting *"with a few of my colleagues from Google Chrome, alongside the DeepMind team, too."* - She describes her focus area: *"the intersection" of "web and AI... That is where my focus is these days.* ### Institutional/Organizational Claims - The talk is presented on behalf of Google Chrome, describing a *"proposed web standard"* (WebMCP) currently in *"early preview stage."* - She attributes team-built resources to organizational effort: *"the maze escape game built by our team in Chrome DevRel"* and *"the model context tool inspector... a web extension that our team in Chrome DevRel built."* - She invites audience follow-up: *"we'll be really interested to talking to you afterwards around the DeepMind booth."* ## Intended Audience - **Web developers**: The talk presumes familiarity with HTML, the DOM, JavaScript, accessibility trees, and Chrome flags. Technical implementation details (e.g., *"a register tool function,"* JSON schemas, *"semantic HTML"*) are presented directly without beginner framing. - **Developers building sites intended for AI-agent interaction**: The closing call to action is developer-directed: *"please give it a go, and try making your website agent ready today."* - **Early adopters / feedback-giving practitioners**: *"we want people to try it out. We want feedback... if you find any bugs. We'd love to know that."* - Live conference audience, gauged interactively: *"has anybody already played around with Web MCP? Only a few people."* ## Claimed Scope of Content - Explaining the **WebMCP proposed web standard**: *"how it works, what it does."* - Live demonstrations: a maze-escape game demo and a *"concert website"* demo ("my Vibe Coded demo") showing tool-calling agents. - **Implementation guidance**: two APIs covered — *"the declarative API and the imperative API"* — with code-level examples (form attributes, `registerTool`). - **Comparison with MCP**: *"what is the difference between Web MCP and MCP... complementary to each other."* - **Onboarding/setup instructions**: Chrome 146+/Canary, experimental flags, the *"Model Context Tool Inspector extension from the Chrome Web Store,"* a blog post for the *"early preview program,"* and a *"GitHub repository"* with demos and an *"eval cli tool."* ## Demonstrated Presentation Patterns - **Live-coding/live-demo style** with acknowledged fallibility: *"Hopefully, Gemini treats me well today"*; a notification is read aloud humorously: *"Great, I'll put that on the Google's credit card."* - **Candid status disclosure** about the API's maturity: *"This API is very experimental. It will change... the code that I've shown might be different next week."* - **Educational framing with relatable use cases**: *"trying to find the right black faux leather clutch bag that can fit your mobile phone in."* - **Prerequisites-first philosophy**: *"making your site accessible for everyone makes it accessible to AI agents by default... you're already halfway to getting an agent-ready website."* ## Relationship to Channel Identity (Observed Only) - The video demonstrates a channel/institutional presence tied to **Google Chrome DevRel**, positioned at the intersection of web platform standards and AI agents, addressing developer audiences with experimental, early-stage technology and actively soliciting community feedback. - No broader channel-wide claims (e.g., series structure, subscriber base, upload cadence) are present in this material. # Delivery Observations: Tara Agyemang — "The agent-ready web: Simplify user actions with WebMCP" ## Tone & Overall Delivery Style - **Conversational and friendly** throughout, opening informally with an audio check and greeting: > "Hello, hello. Hello, can you hear me okay? Okay, cool. Let's get started." - **Candid and humorous asides**, especially during live demos: > "Hopefully, Gemini treats me well today." and > "Great, I'll put that on the Google's credit card." and > "Cuz VIP only for me." - **Candid self-assessment of her own demo**: > "back to my Vibe Coded demo" — a casual, honest framing of her own work. ## Vocabulary & Terminology Patterns - **Technical vocabulary used consistently and accessibly**: "AI agent," "tools," "JSON schema," "accessibility tree," "DOM," "token-heavy." - **Analogy-driven explanation**, a repeated teaching device: - > "You might have heard references to this as the USB-C of AI agent interactions." - > "I like to think of it of as how JavaScript is inspired by Java." - **Relatable everyday scenarios** to ground technical points: > "you're trying to find the right black faux leather clutch bag that can fit your mobile phone in." ## Recurring Phrases & Connective Speech Habits - **"So" as a dominant discourse marker**, appearing at the start of many sentences: > "So, we are going to be talking a little bit about Web MCP." / "So, let's see it in action." / "So, those are the two APIs." - **Repeated "And so" transitions** when chaining explanation steps: > "And so, because it's an AI agent, it can understand a whole bunch of different things." - **"Kind of" as a hedge/softener**: > "Web MCP is kind of inspired by MCP." / > "WebMCP kind of unlocks this new approach." - **"A bit of / a bunch of" quantifiers** used frequently: > "a bunch of new tools," > "a whole bunch of different things," > "a bit more of the background." - **"Just" as a softening adverb**: > "just to give you a bit of an example," > "you can just add a few attributes." ## Teaching Habits & Structural Patterns - **Audience check-in at the start** to calibrate level: > "Has anybody already played around with Web MCP? Only a few people. Okay, great." - **Problem → solution structure**: a long walkthrough of agent pain points ("It can be brittle, and I don't even want to guess at how many tokens you probably just used") before introducing Web MCP. - **Live demo narration in step-by-step detail**, describing what the agent is doing in real time: > "the AI agent, Gemini in our case, has called that tool start game." - **Practical guidance and caveats for developers**: > "you want to make sure you have really descriptive descriptions," > "you always want to make sure that your UI is in sync with the tool calls." - **Honest uncertainty about the experimental status** repeated explicitly: > "This API is very experimental. It will change. It has been changing over the past few weeks. And so, the code that I've shown might be different next week." - **Calls to action closing each section**: > "please give it a go, and try making your website agent ready today." ## Communication Patterns - **Step-enumeration habit** when explaining processes: the agent "will probably look at the HTML... Then it will look into the accessibility tree... then maybe it'll take a screenshot..." - **Comparative framing** for the MCP vs. WebMCP distinction: "whereas MCP enables AI agents to connect to applications on the server side... Web MCP is different in that..." - **Filler and self-correction moments** visible in the transcript ("a a proposed web standard," "the the AI saw the tool"), suggesting spontaneous, unscripted live delivery. *(Uncertainty note: without audio, it's unclear whether these are hesitations or restarts mid-sentence.)* ## Uncertainty Notes - Transcript-only evidence: pacing, gesture, and vocal emphasis cannot be observed. - Some repeated phrasing ("kind of," "so") is common in conference speech generally; the frequency here is observable but its consistency across other talks by this speaker is unknown. # WebMCP: Simplifying User Actions for AI Agents — Position Record **Speaker:** Tara Agyemang, Developer Relations Engineer, Google Chrome team (per curated metadata; transcript self-identifies as "Tara... part of the Google Chrome team," presenting alongside Google DeepMind colleagues). ## Central Claims - **The web was built for humans, but agents increasingly use it on humans' behalf** — and current agent interaction is inefficient: - Agents must parse the entire DOM, inspect the accessibility tree, take screenshots, and compute click coordinates — a process Agyemang describes as **"quite long"** and **"brittle."** - She notes high token usage ("I don't even want to guess at how many tokens you probably just used") and fragility from layout shifts (e.g., ads pushing content and misaligning agent clicks). - **Web MCP (Web Model Context Protocol) is a proposed web standard** that lets sites define their capabilities as **structured tools for AI agents**, referenced as **"the USB-C of AI agent interactions."** She claims it **"significantly improves the performance and the reliability of agents navigating your website."** ## Foundational Prerequisites (Stated Qualification) - Before adopting Web MCP, sites should first improve **web foundations**: - Semantic HTML, robust accessibility standards, page performance (Core Web Vitals), good UX flows. - Claim: making a site accessible for humans **"makes it accessible to AI agents by default."** - Explicit sequencing: **"It's only once you have those in place that it makes sense to start thinking about Web MCP."** ## Web MCP vs. MCP — Distinction - **Complementary, not competing**: MCP connects agents to **server-side applications** (agent accesses anytime, anywhere); Web MCP is **client-side only** — browser window must be open. - Agyemang's analogy: Web MCP is inspired by MCP **"how JavaScript is inspired by Java."** It implements the **tools part of MCP** for in-browser agents. ## Demonstrations - **Maze escape game** (built by Chrome DevRel): - Usable only via AI tooling, not by UI clicks; tools listed per page scope (start game, move N/S/E/W, look, pick up/drop/use items). - Agent matches natural prompts to registered tools; iterative prompts like "complete the maze" work, though she qualifies: **"this specific prompt was not the most efficient"** — refining prompts (e.g., stating the exit location) improves efficiency. - **Concert ticket site** (her own "Vibe Coded demo"): - Agent chained three tool calls: `search concerts` → `open concert page` (using returned ID) → `purchase ticket` (quantity, section). - Noted the demo **"works much better with Gemini 3.1"** (vs. Gemini 1.5 earlier). - Advice: **keep the UI in sync with tool calls**; in production, checkout/payment should remain a **manual user step** so users know they're spending real money. ## APIs - **Declarative API**: add attributes (tool name, description) to HTML forms; browser auto-generates a JSON schema; includes extras like an `agent-invoked` boolean. Suited to standard forms. - **Imperative API**: `registerTool` with manual schema, descriptive descriptions (so the agent knows when to call it), an `execute` block of ordinary JavaScript, and a return value informing the agent's next step. Suited to complex, multi-step flows. - Her assessment: the **imperative API is "probably the one that is most used."** ## Status and Caveats (Preserved Uncertainty) - Web MCP is in **early preview**; the API is **"very experimental"** and **"will change"** — shown code "might be different next week." - Setup requires **Chrome 146+** (recommends Chrome Canary), an experimental testing flag, and the **Model Context Tool Inspector** extension (Chrome Web Store, built by Chrome DevRel). - Resources: early preview program blog post (documentation, best practices) and a GitHub repo with demos (~six or seven) and an eval CLI tool. ## Closing Position - Agents already use the web; developers **"don't have to settle for token-heavy, brittle screen-scraping."** - Web MCP tools can **"turn every website into a high-performance API for agents"** while building good user experiences. - Call to action: try it, report friction points and bugs, so Google can iterate and move Web MCP toward more users. # Speaker Attribution Record ## Video **Title:** The agent-ready web: Simplify user actions with WebMCP — Tara Agyemang, Google **Video ID:** `01a15ca9-0b52-76c1-9df7-98b567a4a6b3` ## Speakers | Speaker | Role | Evidence | |---|---|---| | **Tara Agyemang** | **Creator / Presenter** — sole identified speaker; Developer Relations Engineer on the **Google Chrome team** | Self-identified: *"my name is Tara. I am part of the Google Chrome team... I'm a developer relations engineer"* | ### Non-speaking referenced parties - **Google Chrome DevRel team** — credited by Tara as builders of the maze demo and the Model Context Tool Inspector extension ("built by our team in Chrome DevRel"). - **DeepMind team** — present at the event; Tara notes colleagues are available at the DeepMind booth. No speaking role in transcript. - **Live audience** — present (Tara polls them on Web MCP familiarity); no individual speakers identified. ## Speech Structure - Single continuous presentation by Tara Agyemang; includes **live demos** (maze escape game, concert ticket-purchasing site) delivered in her own voice. - **Opening Q&A with audience** (unidentified attendees): *"has anybody already played around with Web MCP?"* ## Referenced Sources & Resources - **WebMCP** — described as a **proposed web standard** (early preview, experimental). - **Model Context Tool Inspector** — Chrome extension available in the **Chrome Web Store**. - **Main blog post / early preview program** — sign-up grants access to initial documentation, best practices, implementation details, API info. - **GitHub repository** — contains inspector tool, ~6–7 demos (including maze demo code), and an **eval CLI tool**. - **Chrome version 146+ / Chrome Canary** with the Web MCP testing flag required. - **Named products/models:** Gemini in Chrome, Gemini 1.5, Gemini 3.1, Model Context Protocol (MCP). ## Third-Party Claims - Comparison of Web MCP vs. MCP framed by the speaker ("Web MCP is the implementation of the tools part of the MCP"); attributed to Google's presentation, not independent parties. - "USB-C of AI agent interactions" cited by Tara as a characterization "you might have heard" — origin not attributed to a specific source. ## Promotional Segments - **Flagged:** Promotion of Google products and resources — Chrome Canary, the WebMCP early preview program sign-up, the Tool Inspector extension, and the GitHub repository are directly encouraged for audience adoption. - **Flagged:** Invitation to visit the **DeepMind booth** for follow-up conversation. - **Flagged:** Demo sites built by the speaker's own team ("Vibe Coded demo," maze game) showcased during the talk. ## Availability & Limitations - **Comment availability:** Not specified in transcript or metadata. - **Curated metadata:** None supplied beyond title. - **Uncertainty:** Audience responses and any colleagues present are unnamed; no timestamps available in transcript. Hello, hello. Hello, can you hear me okay? Okay, cool. Let's get started. So, we are going to be talking a little bit about Web MCP. Has anybody, just out of curiosity, has anybody already played around with Web MCP? Only a few people. Okay, great. Those few people, you have a bit of a head start, but for everyone else, we'll be going into a bit more of the background, how it works, what it does. So, my name is Tara. I am part of the Google Chrome team. Um I'm a developer relations engineer, and I'm here with a few of my colleagues from Google Chrome, alongside the DeepMind team, too. So, we'll be really interested to talking to you afterwards around the DeepMind booth, if you like have thoughts around web and AI and the intersection between the two. That is where my focus is these days. So, let's get into it. The let's say past few decades, we have been building the web for human actions and human eyes, and we've been trying to optimize for that, but these days, it's not just humans that are using the web. We have agents using the web on the human behalf, too. And we are seeing an increasing number of agents using the web. But, the problem is the agents are having to do so much work to do simple actions on the sites that we've built. And just to give you a bit of an example of this, this is a a website that I've built coded, and it's a concert website for selling tickets for concerts. And we have Gemini in Chrome panel on the side here. And let's say you come along to this website and you've typed this prompt. You want to buy two tickets to the Afro Beats Festival. You've given it the details. The AI agent has to do so much work to make this happen. So, it'll probably look at the HTML because usually the agents will pass the entire DOM just to understand what's happening on your page. Then it will look into the accessibility tree just to understand the structure of your HTML page. Then it maybe it'll take a screenshot of the page, analyze all the different elements that couldn't see in the HTML and the accessibility tree. And then maybe it will measure how far down it needs to click, how far across, where the exact element that it needs to click, and then it'll click that element. And as you can see, this process is quite long. It can be brittle, and I don't even want to guess at how many tokens you probably just used trying to do this. It's probably a lot. And then after all that, maybe your ad has loaded at the top of the page, pushed all your content down, and your AI agent couldn't even click the right place in the end. So, there's so much to think about, but before we go into this proposed web standard, it's worth mentioning that you can do so much by improving web foundations first. So, making your site accessible for everyone makes it accessible to AI agents by default. So, if you improve your semantic HTML, if you focus on robust accessibility standards, and if you improve your page performance, make it load really quickly, think about those core web vitals, and then improve really good user experience flows through your site, you're already halfway to getting an agent-ready website. And it's only once you have those in place that it makes sense to start thinking about Web MCP. So, if you're not already aware, the Web Model Context Protocol is a a proposed web standard, and that gives you the ability to define your site's capabilities as structured tools for your AI agents to use. And so, you might have heard references to this as the USB-C of AI agent interactions. And that's because it instead of any agent guessing what your website does, you're kind of giving the AI agent a menu of tools that it can take of tools that it can use and actions that it can take. And so, because of this, we're seeing that WebMCP significantly improves the performance and the reliability of agents navigating your website. So, let's see it in action. Hopefully, Gemini treats me well today. So, this is the maze escape game built by our team in Chrome DevRel. And just on the side here, we have a Chrome extension. Um I'll show you a link to that afterwards. But, this is the model context tool inspector. And so, we're using this This is a standard Chrome extension extension that lives in your side panel, and it lists out all the tools that it finds on your website. So, at the moment, it only has one It can only see one tool, and that's the start maze game tool. And then at the bottom down here, it gives you two options to interact with the page. So, you can interact via a prompt like a user would prompt normally via the AI agent, or you can call tools directly at the bottom, but we won't be looking at that one today. So, this specific maze game is actually more unique in that you actually can't browse it by clicking around the UI. You can only use this app with the AI tooling. So, let's start a new maze game here. You can also choose your model on the side. So, let's stick with the Gemini 1.5. So, you'll see that at the bottom, when you send a prompt, it gives you all the information. So, the new prompt prompt to start a new maze game, and the AI agent, Gemini in our case, has called that tool start game. The tool itself has returned this information, and then the AI has read that and given me this response. And so, now we have our maze, and you'll notice that on this page, we have a bunch of new tools in the scope of this page, whereas the previous page only had that one tool. This page, we've got a bunch of tools to help us navigate the maze. So, in this maze, you can move around with the north, south, east, west directions. You can look to see where you are in the maze and which directions are open. And then, you can pick up items, drop items, use items as you navigate this maze. And if I pop in some prompts, I can see that I can move down, then maybe after that, then right. The AI agent should use my prompt, match it to the specific tools, so in this case, the move tool. It's taken my direction of down and right, matched that to the north, south, east direction, and sent that off to the tool that we have registered on this page, and then it's moved it down and right. And so, you can do And because it's an AI agent, it can understand a whole bunch of different things. So, I could just say, right, up, maybe right again. Let's try that. And so, the AI agent has seen that uh sounds for right, mapped that to the direction, and then called the move tool with those information. And because it's an AI agent, it can just keep repeating the same tool tool calls until it thinks that it's done what needs to be done. So, I could even say, complete the maze. And then the AI agent should use all the tools available to just keep moving around the maze to pick up items, to use the items when it needs to because it has all the information in the tools available. This specific prompt was not the most efficient, so sometimes you'll see it'll go backwards all the way to the start and then go forwards again. But, the more that you refine the prompt, the better the agent knows how to complete the maze in the most efficient way. For example, if you just say, the exit is in the bottom right corner, it'll be more efficient in its instructions to get to that to that direction. So, I won't I won't continue this cuz it can take quite a while to complete this maze. But, if we go back to the slides here, so this is the model context tool inspector that I mentioned. So, this is the web extension that our team in Chrome DevRel built. The QR code there is is if you want to see where that is in the Chrome Web Store, but anyone can use that and grab it from the Web Store. But, essentially, WebMCP kind of unlocks this new approach to using the web where your users don't have to spend a lot of time trying to figure out how to use more complicated sites. And they can figure out their own workflow. So, they can choose to browse your website the normal way for a bit, then they can hand over control to their AI agent, and the AI agent takes steps on their behalf. And then your user can come in at any time to take control again and browse your site again the way they normally would. And so, that ability to simplify user journeys and make those user journeys for people easier has been a large part of the reason we've seen interest and excitement in this new standard. So, I want to pause for a minute just to address the question that some people have. And that's what is the difference between Web MCP and MCP. But you can kind of see them as being complementary to each other. So, whereas Web So, whereas MCP enables AI agents to connect to applications on the server side, and you'd need to set up your own service for the agent to access, and then the agent can access the information anywhere, anytime, Web MCP is different in that it's kind of inspired by MCP. I like to think of it of as how JavaScript is inspired by Java. And that's in short, Web MCP is the implementation of the tools part of the MCP. And so, Web MCP allows engineers to provide tools to in-browser AI agents. And it's very specific for the client-side features. So, you have to have your browser window open for Web MCP to work, and then you can use it to help your agent interact with the browser. So, all of the tools live in the browser. But you can imagine this for quite a few different types of use cases. So, imagine those websites that are really complicated and have a lot of steps that a user needs to take, maybe like booking a flight or filtering products on a normal shopping website, or filling in complicated medical forms or financial forms, or to trigger fixes that need to be hidden on a page, that are hidden on a page. Or if you're like me, you're just on a normal shopping site and you're trying to find the right black faux leather clutch bag that can fit your mobile phone in, and instead of going through all the little filters, you just want to ask your AI agent to do it for you. So, these are a bunch of examples where any user can ask whatever AI agent they are using to complete these things on their behalf. So, the user doesn't have to manually do this. And they don't have to fill in each input, they don't have to select each checkbox, and using Web MCP in these cases can mean that you can make those actions much easier for users. So, let's look at the APIs. Web MCP proposes two approaches for implementation. So, we've got the declarative API and the imperative API. Let's start with the declarative API. So, if you have a normal HTML form, you can just add a few attributes to the HTML to get this to work. So, we've got the tool name and tool description here, and then your browser will automatically generate a JSON schema that the agent can use to read using the form fields as parameters for the tool. So, here's an example of what the JSON schema would look like for this form HTML. And there are a whole bunch of other attributes that can be used. So, there's like um an agent invoked Boolean attribute, so you can tell whether your form was filled in by a agent or if it was filled in by a human. And there's lots of like more specific attributes that can be used for things like that, too. But essentially, you want to use the declarative API when you have a standard form element. But when you have something more complicated, that's where you want to go back to the imperative API. So, this is where you can register and define your own custom tools for when you have more complex, maybe multi-step UI flows. So, here is an example. So, at the bottom, we have this register tool function. And when you call register tool with an object like this, you need to manually create your own schema similar to the one that we had in the declarative API that was generated. You name your tool and give it the description, and you want to make sure you have really descriptive descriptions that enable the AI agent to know when it should be calling this tool. And then, you have the execute block, which is essentially where you call normal JavaScript. So, maybe you already have functions that you're using that you can call in here, maybe do a light wrapper. In this add to do item example, you can like validate and trim text input, for example, and then you create the DOM elements or DOM nodes and add them to your page. And then, you want to return some information to the AI agent so it knows what happened if everything happened successfully so it can use that information for its next steps. So, those are the two APIs. The imperative API is probably the one that's most used because people have more complex UI flows that it wants the agent to complete. But if we go back to my Vibe Coded demo, I have added a few tools here. So, we have a few featured events in the demo, and then all of the events available down here, and then you can go in and purchase tickets for an on an individual concert page. So, I have noticed that this works much better with Gemini 3.1, so I'm going to try that one. If we wanted to buy tickets to one of these festivals, let's buy tickets to the Summer Vibes Festival. Summer Vibes Festival. Uh let's see, two VIP tickets. Cuz VIP only for me. Send that prompt. So, the A the AI saw the tool search concerts, which it has called to find the specific concert via the concert name, and the tool returned the information about the concert including the ID for that concert. Then it has called the second tool open concert page with the concert ID, and that has opened the Summer Vibes Festival page, and then this new page has separate tools. This one here called purchase ticket, and it's called that in the third tool call here with a quantity two and section name, and then we've got a little notification saying, "Oh, you've bought your tickets. You spent £356. Great, I'll put that on the Google's credit card." But you can see as well like in each step, it's updated the UI to make sure the user can also see what's happening. So, you also you always want to make sure that your UI is in sync with the tool calls that are happening. So, we've got the VIP selected, we've got the quantity selected, and then in real life it would go through to some checkout page. You'll probably want your user to manually do that step so they know that they're spending real money. Let's hit back. So, if you're interested in trying this out, it's probably worth just understanding the status of where we're at with Web MCP. So, we're still in early preview stage. This API is very experimental. It will change. It has been changing over the past few weeks. And so, the code that I've shown might be different next week. But that's because we want people to try it out. We want feedback. We want to know the best way to use this API. And if you're interested in doing that, these are a few steps to get set up. So, Web MCP is enabled in Chrome version 146 upwards. I recommend using Chrome Canary just so you can keep things separate. Otherwise, in the normal Chrome, you have to enable experimental flags. And you might not want to do that on your normal your normal browser. Once you have Chrome Canary, you'll need to enable the Web MCP testing flag with by putting this flag in your URL. And then install the Model Context Tool Inspector extension from the Chrome Web Store that I mentioned earlier. Just so you can play around and debug and see what your tools are doing. Then, uh these are the two resources that I recommend taking a look at. So, this is our main blog post that gives you information on the early preview program for Web MCP. So, if you sign up there, you get access to all of our initial documentation. And you get extra information about the program, information on best practices, implement all the extra implementation details that you you might want to use while you're testing it out, and all of the API information. That is the first one, and the second one is the GitHub repository of all the tools. So, we've got the inspector tool here. We've got all the demos, so you can see the maze demo code is live there for you can to play around with. There's about six, seven different demos you can try out, and there's an eval CLI tool you can use to help you start testing your own sites in the Web MCP tools on your own sites today. So, I mentioned we're still in early preview. That's cuz and we're looking for feedback. So, try it out. Let us know what you think, if you have any friction points, if you find any bugs. We'd love to know that so we can keep iterating on this API, and eventually move onto the next stage and start getting Web MCP in front of more users. But to wrap up, AI agents are already using the web. We don't have to settle for these token-heavy, brittle screen-scraping processes that we have today. Instead, we can use Web MCP tools to turn every website into a high-performance API for agents, and at the same time build incredible user experiences for the users of our sites. So, now that you have the tools and the context, please give it a go, and try making your agent try making your website agent ready today. Thank you very much.