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PopNx MCPMCP servers for people who use AI · explained in plain words

Embodify

by YidaYangversion 0.1.0a2i★ 5iRuns on your computer
Registry nameio.github.YidaYang/embodify

How to install

Overview

Description from the maker (original English)

Give your agent a body: observe and control simulated robots (LIBERO, RoboDojo) over MCP.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Other

Install

Pick the AI app you use, then copy the settings.

Passed 2/2 checks · Runs on your computer

First: which AI do you use?

Pick your app and the steps appear below.

Works with Claude, with some setup first

Install Python (uv) on your computer first (one time) · Only the Claude desktop app (Windows or Mac), not the web or phone

  1. Install Python (uv)

    One time only; it works for every server that needs Python (uv)

    Open the Python (uv) download page ↗

  2. Open the Claude desktop app’s settings file

    SettingsDeveloperEdit Config

  3. Paste these settings and save the file

    {
      "mcpServers": {
        "embodify": {
          "command": "uvx",
          "args": [
            "embodify-mcp"
          ]
        }
      }
    }
  4. Quit Claude and open it again

  5. Try it

    Use Embodify and tell me what you can do for me, then show me one thing

    ✓ If Claude says it is using Embodify or shows a Embodify tool in the answer, you are connected

Menu names checked against Anthropic’s help page on Nov 11, 2026 · your screen may differ slightly by app version

ChatGPT cannot connect Embodify yet

Embodify runs on your computer, but ChatGPT can only connect servers that run on the web

What you can do instead

A · Use an app that can connect Embodify

  • Install Python (uv) on your computer first (one time) · Only the Claude desktop app (Windows or Mac), not the web or phone
  • Install Python (uv) on your computer first (one time)
  • Install Python (uv) on your computer first (one time)
  • Install Python (uv) on your computer first (one time)

B · Others in Other that work with ChatGPT

Picked from the most used in the same category, only ones that connect by address with no key

Works with Claude Code, with some setup first

Install Python (uv) on your computer first (one time)

  1. Install Python (uv)

    One time only; it works for every server that needs Python (uv)

    Open the Python (uv) download page ↗

  2. Open Terminal

    Mac: press Command+Space and type Terminal · Windows: open PowerShell

  3. Paste this command and press Enter

    claude mcp add embodify -- uvx embodify-mcp
  4. Type /mcp in Claude Code

    You should see Embodify listed as connected

  5. Try it

    Use Embodify and tell me what you can do for me, then show me one thing

    ✓ If Claude Code says it is using Embodify or shows a Embodify tool in the answer, you are connected

Menu names checked against Anthropic’s help page on Nov 11, 2026 · your screen may differ slightly by app version

Works with Cursor, with some setup first

Install Python (uv) on your computer first (one time)

  1. Install Python (uv)

    One time only; it works for every server that needs Python (uv)

    Open the Python (uv) download page ↗

  2. Click this button to open Cursor

    Open in Cursor

    Cursor asks you to confirm; click Install

  3. Open the AI chat in Cursor in Agent mode

  4. Try it

    Use Embodify and tell me what you can do for me, then show me one thing

    ✓ If Cursor says it is using Embodify or shows a Embodify tool in the answer, you are connected

Menu names checked against Cursor’s help page on Nov 11, 2026 · your screen may differ slightly by app version

Works with VS Code, with some setup first

Install Python (uv) on your computer first (one time)

  1. Install Python (uv)

    One time only; it works for every server that needs Python (uv)

    Open the Python (uv) download page ↗

  2. Click this button to open VS Code

    Open in VS Code

    VS Code asks you to confirm; click Install

  3. Open the AI chat in VS Code in Agent mode

  4. Try it

    Use Embodify and tell me what you can do for me, then show me one thing

    ✓ If VS Code says it is using Embodify or shows a Embodify tool in the answer, you are connected

Menu names checked against VS Code’s help page on Nov 11, 2026 · your screen may differ slightly by app version

Other apps that use MCP, such as Windsurf, Zed or Gemini CLI, each add servers differently. See how in the app list.

See every app that uses MCP →

JSON settings are under “For developers” below

Stuck? Ask people who use Embodify💬 Discussion →
For developers: Terminal commands and JSON settings

Installed on your computer

Claude Code

claude mcp add embodify -- uvx embodify-mcp

Claude Desktop (claude_desktop_config.json)

{
  "mcpServers": {
    "embodify": {
      "command": "uvx",
      "args": [
        "embodify-mcp"
      ]
    }
  }
}

Cursor (~/.cursor/mcp.json)

{
  "mcpServers": {
    "embodify": {
      "command": "uvx",
      "args": [
        "embodify-mcp"
      ]
    }
  }
}

VS Code (mcp.json)

{
  "servers": {
    "embodify": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "embodify-mcp"
      ]
    }
  }
}

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

At a glance

Downloads per weeki
30/wk
Licencei
No licence stated
Connection
Runs on your computer
Latest versioni
0.1.0a2
Categoryi
Other
Published to registryi
Oct 2, 2026
Code last changedi
Oct 2, 2026
Maker
YidaYang
Packages
pypi: embodify-mcp
Website
github.com
Registry status
Active

Which apps can use it

Based on how this server connects and on each app’s own docs, checked Nov 10, 2026.

Run on your computer in

ClaudeGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatRaycast

All apps, with pros and cons →

Related guides

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 11, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account YidaYang, which owns the repo

  • ✓ Pass

    Still maintained

    Code last changed 40 days ago

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Discussion

Ask, share a tip or report that it does not work. Sorted by what people marked helpful.

No posts yet. Be the first to ask or share.

Posts here are written by users, not the PopNx team.

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User reviews

No reviews yet

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Compared with others in Otheri

  • More stars than 95% of the other 19183 servers in this category
  • Published to the registry more recently than 53% of the other 19183 servers in this category
  • Basic checks equal to or better than 100% of the other 19183 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Embodify safe?

It passed 2 of 2 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed 40 days ago, and the latest version (0.1.0a2) reached the registry 40 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: World Monitor, Claude Flow, Tldraw

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

World Monitor

Real-time global intelligence on conflict, markets, energy, trade, shipping and cyber. 102 tools.

by koala73Widely used★ 90.5KUpdated 6 days ago

Runs in the cloud✓ worldmonitor.app

Claude Flow

AI orchestration with hive-mind swarms, neural networks, and 87 MCP tools for enterprise dev.

by ruvnetWidely used↓ 13.5K/wk★ 81.4KUpdated 14 months ago

Runs on your computer

Tldraw

Draw and visually collaborate with your agents on tldraw's canvas.

by tldrawWidely used★ 51.4KUpdated 8 months ago

Runs in the cloud

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Embodify

Give your agent a body.

Your best embodied agent is your favorite agent.

Embodify lets the agent you use every day — Claude Code, Codex or any other MCP-capable agent — see and control robots directly, and keeps everything else that makes it yours. Bring in frontier models with embodied manipulation skills, such as GPT-6 Astra and Claude Opus 5.5, and let your AI companion step into the physical world.

中文 · One-line setup · Backends · For research · Skills · Roadmap

First release (0.1.0a1). Supports LIBERO and RoboDojo today; support for

RoboTwin and the LeRobot SO-101 arm is coming soon.

Why Embodify

Most embodied agents are built from scratch: a dedicated harness wraps a model, hands it a fixed set of robot actions, and nothing else. The agent you use every day already has what those harnesses lack:

  • Context and memory. It manages long sessions and remembers across them.
  • It knows you. Your preferences, your projects, your lab setup.
  • It talks with you in the terminal, IDE, desktop app or chat you already use. You can ask how it is going, step in, correct it, or teach it mid-task.
  • It has a computer. It writes and runs code, uses its tools, searches the web and reads papers.

Embodify keeps all of that and adds a body. Install the plugin and your agent gets robot observation and control tools over MCP — a protocol it already uses for everything else — plus skills that teach it how to operate robots and how to achieve recursive self-improvement (RSI) from its own experience.

Why "best"

A general agent with a body is stronger than a harness that can only move a robot:

  1. It can think with tools, not only act. When a task needs geometry it can write a script; when it needs a fact it can search; when it needs perception it can run a model.
  2. It keeps track. Long-horizon manipulation fails when the agent forgets what it already tried. Mature agents manage context and memory well.
  3. It works with you. It asks you when a task is ambiguous, takes your guidance when it gets stuck, and remembers your corrections next time.
  4. It controls robots in its native language. Robot control arrives as ordinary MCP tool calls, the same shape as every other tool the agent uses.
  5. It builds on frontier embodied models. Your agent runs on models with frontier embodied manipulation skills, such as GPT-6 Astra and Claude Opus 5.5, and every model upgrade makes your robot better, with no retraining.
  6. It improves itself recursively. It turns every episode into lessons, lessons into rules and rules into new skills, and gets better the more it works.
  7. Zero-shot, few-shot and in-context learning come easily. A general agent takes on new tasks without training: describe a task in plain language (zero-shot), show it a few examples (few-shot), or put instructions, demonstrations and past experience in its context (in-context learning, ICL).

What's inside

Embodify is an agent plugin with two parts, and installing the plugin gives your agent both:

PartWhat it gives your agent
MCP server (embodify-mcp, registered as embodify)Tools to list tasks, start an episode, observe cameras and robot state, move end effectors, open and close grippers, and coordinate two arms.
SkillsKnow-how: running a careful observe–act loop, keeping a profile of the robot body and cameras, and learning from past episodes. More perception skills are coming soon.
flowchart LR

A["Your agent Claude Code · Codex · …"] -- "MCP (stdio)" --> B["embodify-mcp episodes · budgets · logs"] K["Embodify skills"] -. "loaded by" .-> A B --> I["Backend interface"] I --> L["LIBERO"] I --> R["RoboDojo"] I --> T["RoboTwin (WIP)"] I --> H["SO-101 and other real robots (WIP)"] I -. "SSH / TCP" .-> G["Remote server"]

The MCP server is the front end. Each simulator, benchmark or robot is a backend behind one small interface, so adding a new one does not change what the agent sees. Highlights:

  • Works with any MCP host. No LLM calls inside; your agent keeps its own model, memory, skills and tools.
  • One call, one motion. The control loop runs next to the simulator. Each action returns the actual displacement, remaining error, a stop reason and fresh camera images.
  • Remote simulation. Run the simulator on your lab's GPU server over SSH while the agent stays on your laptop. Heartbeats keep high-latency links alive; if the link drops, the episode aborts cleanly and reset_task reconnects.
  • Fair evaluation. When you use Embodify to evaluate an agent's manipulation skills, task success is recorded for humans and never exposed to the agent.
  • Live monitor and replay. Watch running episodes live in a browser, or replay past ones frame by frame.

Backends

BackendRobotStatus
fake, fake-two-armKinematic diagnostic, one or two arms✅ Included, no simulator needed
liberoFranka Panda, 130 tasks in 5 suites✅ Included
robodojoDual ARX X5, all 54 simulation tasks✅ Included
RoboTwinDual-arm manipulation benchmark🚧 Coming soon
LeRobot SO-101Real robot: 5-DoF arm with gripper🚧 Coming soon
remoteAny backend above, on another machine✅ SSH or trusted TCP

Coming soon: robot arms such as xArm, mobile manipulators, humanoids and dexterous hands. If there is a simulator or robot you would like us to support, open a GitHub issue with details about it. We welcome contributions of new backends.

For research

Embodify doubles as a testbed for the embodied manipulation abilities of language models and agent frameworks. Run GPT, Claude, Gemini, Qwen and other models, through Claude Code, Codex or your own agent framework, on the same simulated benchmarks with the same tools, and compare them directly.

  • Standard benchmarks: LIBERO (130 tasks in 5 suites) and RoboDojo (54 tasks), with RoboTwin coming soon.
  • Fair by design: task success is recorded for evaluators and never shown to the agent, and --lock-task fixes the scene.
  • Controlled budgets: episode budgets and per-call step limits are configurable and logged.
  • Complete records: tool calls, robot states and camera frames are saved for replay and analysis.

Typical studies include zero-shot robot manipulation by frontier models, comparisons of agent frameworks on the same model, the effect of skills and memory, and recursive self-improvement across episodes. See citation for how to cite Embodify and what to report.

Quick start

One-line setup

Paste this into Claude Code, Codex or any other agent you use:

Install Embodify (https://github.com/YidaYang/embodify) for yourself, then set up a LIBERO environment or connect to my existing simulator.

That's it. Your agent installs the Embodify plugin, sets up the simulator and tells you when to restart the session. (Agents: follow the agent setup guide.)

Manual install

Embodify starts its MCP server with uv, straight from PyPI. If you do not have uv yet, install it with python -m pip install uv or the official installer.

Claude Code
claude plugin marketplace add YidaYang/embodify

claude plugin install embodify@embodify

Codex
codex plugin marketplace add YidaYang/embodify

codex plugin add embodify@embodify

The plugin brings both the MCP server, registered as embodify, and the skills. The server starts on the Fake backend, so you can try the tools right away; connect a real simulator next. It reads its settings from ~/.embodify/config.json (see backend setup). To check the package on its own, without an agent:

uvx --from embodify-mcp embodify-mcp-smoke   # end-to-end check: no simulator, GPU or model needed
Other agents
  1. Register the server in your agent's MCP configuration using examples/mcp.json.
  2. If your agent supports Agent Skills (SKILL.md folders), copy or link the folders in plugin/skills/ into its skills directory.

Restart the session, then ask your agent something like *"Reset the task, describe what the cameras show, then raise the gripper 5 cm."*

Connect a real simulator

Let your agent do this step. Paste:

Connect Embodify to a real simulator for me: set up LIBERO on this machine, or connect to my existing simulator. Follow https://github.com/YidaYang/embodify/blob/main/docs/agent-setup.md.

Your agent installs the simulator or connects to yours over SSH, writes the server settings to ~/.embodify/config.json and asks you to restart the session. To do it by hand, see backend setup.

Watch robots live and replay episodes

Add "monitor-port": 8765 to ~/.embodify/config.json and open http://127.0.0.1:8765 to watch the robot work live, with every camera view, the robot state and each tool call your agent makes, or to replay any past episode frame by frame. To browse saved runs without a running server:

uvx --from embodify-mcp embodify-mcp-monitor   # reads ~/.embodify/runs

The monitor listens on localhost only.

MCP tools

ToolPurpose
get_session_infoCurrent run, task, step budget and robot state; no images
list_tasksBrowse the backend's task catalogue
reset_taskStart an episode and return the first camera images
observeCamera images and robot state, without moving
move_relativeMove one end effector by a translation and optional rotation
set_gripperOpen or close a gripper
control_armsMove several arms together with common progress (two-arm backends)
stop_episodeEnd the episode and write its record

Translations are in meters, rotations in radians, quaternions in xyzw order. Frames and stop reasons are defined in the action contract.

Skills

The plugin contains these skills:

SkillStatusWhat it does
embodied-controlv0The observe–act loop: small moves, reading stop reasons, verifying grasps, releasing in a separate call
robot-profilev0Keep a profile of the robot: arms, cameras, frames, calibration and measured behavior
task-experiencev0Write a lesson after every episode, read relevant lessons before the next, promote repeated lessons to rules
object-segmentationComing soonOpen-vocabulary segmentation of camera images
depth-rangingComing soonPixel to 3D position from depth and calibration

Skills are grouped into three families that will keep growing:

  1. Embodiment knowledge: how this particular robot is built, where its cameras are and how it actually moves.
  2. Manipulation tools: perception, measurement and planning tools, including ideas from work such as Code as Policies.
  3. Recursive self-improvement: turning experience into lessons, lessons into rules and eventually into new skills, improving recursively.

Roadmap

  • Backends: RoboTwin; LeRobot SO-101 as the first real robot, with workspace limits, human-judged success and an emergency stop;

Only the beginning is shown; read the rest in the repo