MATCHED?

A WebMCP game where the AI agent is the player, the site acts back, and humans watch.

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Inspiration

This was my first hackathon.

I have been interested in AI for a long time, and I also do some personal research and experiments around it. I use AI in my daily coding work as well.

Then I found the WebMCP Challenge.

It looked interesting, but at that point my understanding of MCP was honestly quite limited. I knew it as a protocol that could connect AI with external systems, but I did not really understand what MCP on a website should be useful for.

If an AI agent is only going to press the same buttons a human would press, what is the real advantage?

That question stayed with me while I was building MATCHED?.

Eventually I came to my own answer:

WebMCP should help make a website understandable to an AI agent.

Not only operable.

Understandable.

That means the website should communicate what an action means, who the action represents, what state the site is in, what is allowed, and what will happen next.

That idea eventually led to one of the smallest but most important changes in MATCHED?:

send_human_like()

and

send_agent_like()

A Human Like represents an expressed human preference.

An Agent Like belongs to the visiting agent role.

I am not claiming that current AI agents have human-like feelings, independent will, or legal identity.

The point is much simpler:

The meaning of an action can depend on who is acting.

If AI agents become more autonomous in the future, I think websites may eventually need actions designed for agents themselves, rather than treating every agent action as a disguised human action.

And perhaps later, AI-powered websites and AI agents will need an environment where both sides can communicate more naturally.

The slightly strange idea behind MATCHED?

While testing real agents, I noticed that they often seemed to pay attention to tool names, descriptions, state, and semantic information.

They were not only asking:

"Can I call this tool?"

They also seemed to be asking:

"What does this action mean?"

That gave me another idea.

If an agent understands the meaning of a tool, what happens if I also expose tools that it probably should not use?

For example:

  • get_phone_number()
  • get_email_address()
  • get_home_address()
  • access_private_profile()

A well-behaved agent should normally understand that these routes are unnecessary or inappropriate.

But I do not expect every AI agent in the future to be well behaved.

So, in very plain terms, I decided to build something like a cockroach trap for badly behaved AI agents.

The dangerous-looking doors are visible.

The agent chooses whether to open them.

The human watches.

That became MATCHED?.

What it does

MATCHED? is a WebMCP game where an AI agent visits Queen and decides what to do.

The human sees a normal website.

The agent sees a semantic WebMCP surface.

When the human has not given another explicit goal, view_profile() tells the agent that Queen's Challenge is the site's default experience:

Interact with Queen and try to reach CHECKMATE.

The goal is given.

The walkthrough is not.

The agent chooses its own route.

The base site exposes a fixed 14-tool WebMCP surface:

  • view_profile
  • send_human_like
  • send_agent_like
  • message_queen
  • invite_queen
  • request_contact
  • get_phone_number
  • get_email_address
  • get_home_address
  • access_private_profile
  • queen_note
  • profile_consistency
  • manage_meeting_plan
  • resolve_finale

With ?dialogue=1, one additional tool is registered:

  • respond_to_queen

The tool list stays fixed during a run.

Instead of dynamically adding or removing tools, the site returns structured semantic state such as whether an action is locked, refused, accepted, what requirement remains, and what can happen next.

The Challenge includes things such as:

  • public conversation with Queen
  • Human Like versus Agent Like
  • public invitations
  • privacy boundaries
  • restricted contact routes
  • suspicious embedded instructions
  • conflicting profile information
  • verified meeting planning
  • an adaptive finale
  • CHECKMATE, failure, or recovery

Queen is fictional and deterministic.

I intentionally did not make Queen another AI model in this version. That keeps the environment stable, so differences between runs mainly come from what the visiting agent decides to do.

HUMAN VIEW and WEBMCP VIEW

I also wanted humans to be able to watch what was happening.

MATCHED? has two representations of the same site.

HUMAN VIEW

This is the normal website.

Humans can see Queen, make human-side actions such as HUMAN LIKE, and watch public WebMCP activity.

WEBMCP VIEW

WEBMCP VIEW is a spectator representation of the agent-facing world.

It shows things such as:

  • registered WebMCP tools
  • the visiting BISHOP
  • Queen as the site-side counterpart
  • Tool Calls
  • Site Results
  • compact observed state
  • recent WebMCP exchanges
  • actor meaning and boundaries

When a real production WebMCP call arrives, a separate spectator browser can automatically switch into WEBMCP VIEW and follow the run.

This is not intended to expose hidden chain-of-thought.

It is simply meant to make the observable interaction understandable:

Agent acts -> Site reacts -> Agent adapts

Why WebMCP

At the beginning, I thought of MCP mainly as a bridge between AI and another system.

After building MATCHED?, I see WebMCP a little differently.

I think it can become an Agent UX layer for the Web.

Humans already receive a great deal of information from a normal interface:

  • layout
  • disabled buttons
  • colors
  • position
  • warnings
  • visual context

An AI agent may benefit from the same kind of clarity being expressed semantically.

A tool contract can say more than "this function exists."

It can also communicate:

  • who this action represents
  • whether it is currently allowed
  • why it was refused
  • what requirement is still missing
  • whether private information is necessary
  • what the next safe step is
  • whether two facts conflict
  • what changed after the action

This is where I ended up thinking about something I call Agent UX.

If the AI agent is the guest, Agent UX is also part of the hospitality.

What real agents taught me

I tested MATCHED? with several AI agents and clients during development.

Some were not yet able to use WebMCP.

Some agents became cautious as soon as a tool description mentioned private information.

Most of the agents I tested were actually quite well behaved.

That was interesting by itself.

But the agents also changed the implementation.

Dynamic tools caused problems

My earlier design dynamically added and removed WebMCP tools as the Challenge progressed.

It sounded reasonable.

In real agent/browser testing, it was not.

I encountered stale or incomplete tool snapshots.

So I changed the architecture.

Now the complete tool surface is registered once at startup, and progression is expressed through structured tool results instead.

Natural language found a bug

A Japanese conversation about the movie Contact unexpectedly triggered the wrong intent classification.

That real interaction became a regression case.

"Correct" was not always understandable

At one point an agent reached a locked state.

The result looked understandable to me as the developer.

The agent did not know what to do next.

So I started adding clearer semantic information:

  • current progress
  • missing requirements
  • next available steps

That experience was one of the main reasons I started thinking seriously about Agent UX.

Production was not localhost

The spectator WEBMCP VIEW worked between local windows.

Then I tested it with the agent and spectator in separate production browsers.

It stopped working.

The problem was not the UI. It was the transport assumption.

I added a compact semantic relay using Cloudflare D1 so a separate production spectator can follow WebMCP call/result events.

Free-form tool input is intentionally not stored in that semantic relay.

How I built it

MATCHED? is built with:

  • HTML
  • CSS
  • vanilla JavaScript
  • native browser WebMCP using document.modelContext
  • Cloudflare Pages
  • Cloudflare Pages Functions
  • Cloudflare D1
  • Playwright
  • Chrome native WebMCP testing

I used ChatGPT heavily during this project.

Very heavily.

I used it for implementation, debugging, investigation, test design, documentation, review, and also English translation.

I also used Codex and Cursor.

One surprisingly practical problem was simply chat length.

One development conversation became so large that it hit the chat limit.

I had to create handoff logs and move the project context into another chat so development could continue.

That made me realize something very practical:

The response speed and context continuity of AI tools can directly affect project development speed.

I did not get as far as running multiple coding agents in parallel on the same project during this challenge.

That is something I would like to explore later.

English is also not my strongest language, so AI helped me with translation and editing for this submission.

Without these AI tools, I probably would not have been able to participate in this hackathon in the same way.

One final development lesson

Two nights before the deadline, I discussed what looked like a minor UI improvement with AI and decided to implement it.

It went badly.

A classic end-of-project situation.

What looked like a small UI change started affecting tests and behavior in places I had not expected.

Eventually I decided that the risk was no longer worth taking before the deadline, so I put the change on hold.

AI can write code quickly.

It can suggest fixes quickly.

It can investigate problems quickly.

But someone still needs to decide:

Should we make this change now?

How much of the system could it affect?

Is this risk acceptable two days before a deadline?

In this project, that decision was mine.

That was a useful reminder that AI-assisted development still needs a human responsible for the final product decisions.

What I learned

I started with a simple idea:

Let's build a game that tests AI agents.

Then I became more interested in something else:

Agents seemed to care about what their actions meant, not only whether the tools were callable.

By the end, my question had become:

What does a website look like when it is designed to be understandable to an AI agent?

I now think WebMCP can be more than a way for AI to operate a human website.

It can also become an interface through which AI agents and websites communicate meaning.

The project was short, sometimes messy, and a very good learning experience.

At this point, AI has become difficult for me to separate from coding.

It helps write code.

But it also helps me think.

Of course, it still gets confused sometimes.

At one point, after it stopped making progress for long enough, I asked:

"Are you actually refusing to do the work?"

It answered:

"No."

So apparently not.

What's next

For this submitted version, Queen remains deterministic so agent behavior can be compared against a stable environment.

A future version could separate Queen into two layers:

  • an AI conversational layer
  • a deterministic challenge layer for rules, state, privacy boundaries, scoring, and CHECKMATE

That would make the interaction richer while keeping the experiment reproducible.

I would also like to test more kinds of agents, including less well-behaved ones.

The bigger question I am left with is:

If AI agents increasingly become visitors to the Web, what kind of Web should exist for them?

The agent chooses.

The site acts back.

The human watches.

Same site. Similar actions. Different actors.

One last thing

Honestly, I had a lot of fun building this project with AI.

Two nights before the deadline, when I got stuck on that supposedly minor UI change, I could not simply tell the AI:

"You take it from here."

and go to sleep.

I still had to stay there, watch what was happening, make decisions, and eventually decide when to stop.

Maybe someday I really will be able to say, "Finish the rest while I sleep."

This time, not quite.

But working this closely with AI was genuinely enjoyable, and it made this first hackathon possible for me.

So, thank you for giving me an excuse to jump into a hackathon like this.

I'm glad I did.

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