Chiplab
MCP serverAI & modelschiplab lets your AI build, run, and validate firmware on virtual hardware, so firmware work can happen without physical devices on hand. It also includes a hardware knowledge corpus your AI can consult while working. This means firmware development and testing can be handled end to end by your AI.
Use Chiplab in Claude, ChatGPT or Ahel Desktop
Free. Sign in, add Chiplab and connect your AI. About a minute.
Also: Claude Code · Cursor · Codex
Then ask your AI: use Chiplab
Needs your own Chiplab account. You sign in to it and approve access when you connect.
Details
Available today. Use it from your connected AI after setup.
After adding chiplab, ask your AI to build and run a firmware project on virtual hardware, or to look up hardware details in the knowledge corpus.
What your AI can do with it
- Build firmware projects
- Run firmware on virtual hardware
- Validate firmware behavior on virtual hardware
- Answer hardware questions using the built-in knowledge corpus
Install Chiplab
The server’s own address, for the clients that take one directly. Or connect Ahel once and every client you use reads it from one address, with the account kept on Ahel rather than in each client’s config.
Claude Code
claude mcp add --transport http --scope user chiplab 'https://chiplab.veecle.ai/mcp'Run it once in your project, then open /mcp to approve any sign-in the server asks for.
Claude Desktop
https://chiplab.veecle.ai/mcpAdd a custom connector in Settings, paste this address, and approve the sign-in.
Cursor
cursor://anysphere.cursor-deeplink/mcp/install?name=chiplab&config=eyJ1cmwiOiJodHRwczovL2NoaXBsYWIudmVlY2xlLmFpL21jcCJ9Open the link and Cursor adds the server at that address.
ChatGPT
https://chiplab.veecle.ai/mcpIn Settings, enable Developer mode, create an MCP app, and paste this address. Your plan and workspace must allow custom apps.
Codex
codex mcp add chiplab --url 'https://chiplab.veecle.ai/mcp'Run it once, then sign in with codex mcp login chiplab if the server asks for an account.
From the project's README
As published by veecle/chiplab in README.md.
Simulate & validate firmware on virtual instances of real chips, straight from your AI coding agent — no hardware.
Chiplab is a hosted MCP service. Your AI coding agent connects to it, uploads compiled firmware, runs it on a virtual instance of a real microcontroller, and reads back the UART output. You don't drive the toolchain — your agent does.
We're building the next way to develop chips, in the open and early.
Today it runs Hello world! on STM32 and Nordic boards; a lot more is on the way — see veecle.ai.
Try it, poke at it, and tell us what you'd like to see.
Quickstart
-
Create a free account at veecle.ai (no credit card).
-
Connect your agent to the Chiplab MCP server.
-
git clone https://github.com/veecle/chiplab && cd chiplab, then tell your agent:"Set up Chiplab and run the stm32f4-discovery example on it."
Your agent reads AGENTS.md, installs what it needs, builds the firmware, uploads it, runs it on the virtual board, and reports Hello world! back from the chip's UART.
That's the whole loop.
Connect your agent
Chiplab is an HTTP MCP server at https://chiplab.veecle.ai/mcp.
Most clients take the standard mcpServers schema:
{
"mcpServers": {
"chiplab": {
"type": "http",
"url": "https://chiplab.veecle.ai/mcp"
}
}
}
On first use your client opens a browser to sign in. To verify, ask your agent to call Chiplab's discovery/help tool with no arguments.
If you cloned this repo, the server is already configured via .mcp.json — Claude Code will prompt you to trust it.
Otherwise:
claude mcp add --transport http chiplab https://chiplab.veecle.ai/mcp
Use the mcpServers block above in claude_desktop_config.json or .cursor/mcp.json.
Same block in .vscode/mcp.json, but under a servers key instead of mcpServers.
TOML in ~/.codex/config.toml:
[mcp_servers.chiplab]
url = "https://chiplab.veecle.ai/mcp"
Then sign in with codex mcp login chiplab.
How it works
The contract is framework-agnostic: your agent builds an ELF → uploads it → runs it on the target board → reads the captured UART output. Runs return synchronously and are bounded to a fixed amount of virtual time.
This repo ships a ready-to-run example for every supported board, grouped by framework — bare-metal (Rust, vendor HAL), embassy-rust (Embassy async), zephyr-os (Zephyr RTOS, C), freertos (FreeRTOS, C), and threadx (Eclipse ThreadX, C).
The full board × framework matrix is in supported-boards.md.
Toolchain and build details live in each framework's directory (examples/<framework>/README.md for humans, AGENTS.md for agents) — your agent finds them on its own.
Prefer building by hand?
Each framework README has the exact commands.
To run your own firmware, build an ELF for a supported board and ask your agent to upload and run it the same way.
Contributing
Adding a board example is the easiest way in: mirror an existing example for the same framework and add a row to supported-boards.md.
All conventions live in AGENTS.md and each framework's AGENTS.md.
New chip, OS, or peripheral support is server-side — open a request instead of adding an example for an unsupported board.
Found a bug? Missing a chip? Tell us here.
This is Chiplab's public home — bugs, feature ideas, and chip/OS/peripheral requests all belong in this repo's issues. Requests really do shape the roadmap — see veecle.ai/roadmap for what's planned, or come say hi on Discord.
License
MIT © 2026 Veecle GmbH.
Signals
- GitHub stars
- 17
- Forks
- 2
- Last commit
- Sep 2026
Advanced
- Delivery
- chiplab MCP server → your Ahel connector (mcp.ahel.ai) → your AI.
- Item type
- mcp-server
- Key
ai-veecle-chiplab- Source
- github.com/veecle/chiplab
- Hosted endpoint
https://chiplab.veecle.ai/mcp
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