MCP-Forge

Integrating the amazon-mcp we created into poke
Once integrated, asking Poke to use the MCP and let it automatically figure out which tool to use and get product reviews
Home page with marketplace of globally available MCP servers
Detailed view of MCPs available
Dashboard to create a new MCP by providing a website URL
Generating an MCP
Add an existing MCP server to the market place in dashboard
MCP inspector tool to test MCPs, see available tools, and verify correctness in one easy spot
Inspecting the Amazon MCP we created earlier. To the left we can see all the tools
Example output of running a tool that was created
Poke recipe to create a new MCP from within Poke. User requests a new mcp from a url, and we hit our factory-mcp to begin the full pipeline
Integration of our factory-mcp

Inspiration

MCP (Model Context Protocol) is the emerging standard for connecting AI agents to external tools and data sources. But creating an MCP server today requires deep technical knowledge — you need to understand the protocol, write tool definitions, handle browser automation, and figure out hosting. We asked ourselves: what if any website could become an MCP server with a single click?

We were inspired by the idea that the internet is full of useful tools trapped behind UIs — airline booking, e-commerce search, data lookups, government portals. If AI agents could interact with these sites programmatically through MCP, it would unlock an entirely new layer of capability. So we built MCP-Forge: a platform that automatically discovers what a website can do, generates a full MCP server with tools for each capability, deploys it live, and lists it in a marketplace for anyone to use.

What it does

MCP-Forge turns any website into a fully functional MCP server through an automated pipeline:

Discovery — An AI agent powered by Claude Sonnet launches a cloud browser (via Browserbase) and autonomously explores the target website. It navigates pages, identifies interactive elements like search forms, filters, and data tables, and maps out the site's capabilities.
Code Generation — The discovery results are fed to an LLM that generates a complete Python MCP server. Each discovered capability becomes a tool function that uses Stagehand (AI browser automation) to replay the interactions programmatically.
Deployment — The generated server is automatically deployed to Modal as a serverless endpoint, instantly accessible via the MCP Streamable HTTP transport.
Marketplace — The deployed server is registered in our MCP Marketplace, where users can browse available servers, test them with our built-in MCP Inspector, and connect them to any MCP-compatible AI client (Poke, Cursor, Claude Desktop etc.).

The entire pipeline is itself exposed as an MCP server (the "MCP Factory"), which means you can trigger server generation from within Poke or any MCP client — an MCP server that creates MCP servers.

How we built it

The project has four major components:

Discovery Engine (Browserbase + Stagehand + Claude) We use Browserbase to spin up cloud Chrome sessions and Stagehand's AI-powered browser automation SDK to give our Claude agent tools like observe, act, and extract. The orchestrator agent receives a URL and a turn budget, then autonomously explores the site — clicking through navigation, filling forms, extracting data — building a structured map of the site's capabilities as endpoint definitions with parameterized steps.

Code Generation & Deployment Pipeline Discovery results are passed to Claude Sonnet, which generates Python MCP server code using the FastMCP framework. Each endpoint becomes an @mcp.tool() decorated async function that replays the browser interactions via Stagehand. The generated code includes a Modal deployment script that packages everything into a serverless ASGI app with Browserbase secrets injected. We call modal deploy programmatically and parse the live URL from the output.

MCP Factory (Meta-MCP Server) The entire pipeline is wrapped as an MCP server with tools: discover, generate, deploy, replay, and full_pipeline. This server is containerized and hosted on Google Cloud Run, accepting JSON-RPC requests over Streamable HTTP. This is what powers both the web UI's "Generate from Website" feature and the Poke integration — Poke can call full_pipeline directly as an MCP tool.

Marketplace UI (Next.js + Vercel Postgres) A Next.js 14 web app with a neo-brutalist design, featuring user authentication, a dashboard for managing your MCP servers, an API with key-based access control, and a built-in MCP Inspector for testing connections. The marketplace is backed by Vercel Postgres for persistent, globally accessible data.

Challenges we ran into

Non-deterministic browser exploration — Every website is different. Getting the AI agent to reliably discover useful capabilities without getting lost in infinite scroll, popups, or auth walls required careful prompt engineering and budget constraints on browser turns.
Reliable code generation — Generated MCP server code needs to actually work. We iterated on strict templates and validation steps (type-checking in sandboxes) to minimize broken deployments. Getting the LLM to produce correct Stagehand action instructions with proper parameter placeholders was particularly tricky.
MCP protocol handling — Implementing the full MCP handshake (initialize → initialized → tools/call) with both SSE and JSON responses, plus session management, required careful attention to the spec — especially when proxying through our API layer.
End-to-end latency — The full pipeline (browser discovery + code generation + Modal deployment) can take 2-10 minutes. We had to design the UX to keep users engaged during the wait and handle timeouts gracefully across multiple async services.

Accomplishments that we're proud of

An MCP server that creates MCP servers — The recursive nature of the Factory MCP is genuinely cool. You can use Poke to tell our MCP to generate a new MCP server for any website, and minutes later it's live.
Fully automated discovery — The AI agent genuinely explores websites it has never seen before and figures out what they can do. Watching it navigate Amazon, fill search boxes, and map out product search as an MCP tool is magical.
Live deployments in minutes — Going from a URL to a deployed, globally accessible MCP server with zero human intervention.
Built-in MCP Inspector — Users can test any MCP server directly in the browser, making the marketplace actually useful rather than just a listing.

What we learned

Browserbase + Stagehand is incredibly powerful for AI-driven web automation. The observe/act/extract paradigm maps naturally to how you'd describe website capabilities.
LLM-generated code is viable for narrow domains — when you constrain the template tightly enough, code generation is surprisingly reliable. The key is giving the model a strict skeleton and only asking it to fill in the variable parts.

What's next for MCP-Forge

Smarter discovery — Multi-session exploration that can handle auth flows, pagination, and dynamic content more robustly.
Server versioning & updates — Re-run discovery to update tools when websites change.
Community curation — Ratings, reviews, and verified servers in the marketplace.
Self-healing servers — Monitor deployed MCP servers and automatically re-generate when they break due to site changes.