Posecode MCP

Name: Posecode MCP
Author: posecode-dev

by posecode-devversion 0.5.0i★ –iRuns on your computer

Registry nameio.github.posecode-dev/posecode-mcp

Install

Overview

Description from the maker (original English)

Author, ROM-validate, and render 3D human movement (.posecode) with a shareable playground link.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

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: Design, images & audio

Install

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

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: POSECODE_BASE_URL

You need

npmNode.js installed (for the npx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "posecode": {
      "command": "npx",
      "args": [
        "-y",
        "posecode-mcp"
      ],
      "env": {
        "POSECODE_BASE_URL": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add posecode --env POSECODE_BASE_URL=your-value -- npx -y posecode-mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "posecode": {
      "command": "npx",
      "args": [
        "-y",
        "posecode-mcp"
      ],
      "env": {
        "POSECODE_BASE_URL": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "posecode": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "posecode-mcp"
      ],
      "env": {
        "POSECODE_BASE_URL": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

POSECODE_BASE_URL env
Playground origin used for render permalinks (defaults to https://posecode.org).

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

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 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account posecode-dev, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… 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

Compared with others in Design, images & audioi

More stars than 0% of the other 2172 servers in this category
Published to the registry more recently than 28% of the other 2172 servers in this category
Basic checks equal to or better than 100% of the other 2172 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 Posecode MCP safe?

It passed 1 of 1 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 –, and the latest version (0.5.0) reached the registry 2 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Figma Context MCP, Claude Real Video, Figma MCP Server

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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.

Posecode

Kinematic motion as text.

An inspectable, editable movement format for animation tools, LLMs, and web products.

Like Mermaid for movement: small text documents for describing, validating, and rendering

deterministic human motion without hiding the source inside a black box.

Live Playground · Movement Library · Language Specification (SPEC.md) · LLM Authoring Guide · Examples · MCP Server

Why Posecode?

Animation clips and generated trajectories can show movement, but they often hide the semantic decisions that produced it.

Posecode keeps those decisions in readable source. A human can write the document, an animation tool can emit it, or an LLM can draft it. Parsing, validation, editing, and rendering do not require an AI model.

One .posecode document — shoulders: abduct 160 , hips: abduct 30 , repeat 12 — rendered live in the browser.

For example:

Bend your knees, move your hips backward, and keep your chest upright.

A human may understand that instruction, but a renderer cannot reliably determine:

which joints should move,
by how many degrees,
in which coordinate frame,
over what duration,
in what sequence,
or within which physical limits.

Humans, tools, and language models all need a shared syntax for expressing movement in a renderable and testable form.

Posecode provides that missing representation.

See Posecode in 28 seconds

From readable movement source to validated 3D rendering, MCP tools, and a one-script web embed.

▶ Watch the 28-second builder demo

Plain text in. Smooth, programmable 3D motion out.

Movement examples

pelvis: hinge — deadlift

knees: flex 95 — squat

shoulders: abduct 90 — lateral raise

Why Not Diffusion Text-to-Motion?

Neural text-to-motion systems can generate impressive movement, but they introduce problems for lightweight, programmable applications.

Resource intensive

Many systems require large models and GPU-backed inference, making real-time consumer deployment expensive.

Difficult to control

They usually produce coordinate trajectories rather than editable semantic instructions.

It is difficult to request a precise change such as:

Reduce knee flexion by 10 degrees during the second phase.

Hard to validate

Black-box trajectories do not naturally expose readable joint rules, phase definitions, or range-of-motion limits.

Hard to debug

When a movement looks wrong, developers may not know which semantic instruction caused the problem.

The Posecode Approach

Posecode uses a lightweight, text-driven pipeline.

Readable: movements are stored as small .posecode documents.
Structured: joints, actions, angles, timings, and constraints are explicit.
Editable: developers and models can modify individual movement properties.
Fast: parsing and rendering happen client-side.
Deterministic: the same Posecode input produces the same validated representation.
Inspectable: parser warnings and fidelity checks explain problems.
Agent-friendly: the MCP server exposes generation, validation, critique, and sharing tools.
Safety-aware: authored and IK-generated angles are constrained by configured range-of-motion limits.

The Idea in 30 Seconds

A .posecode file describes movement as timed phases with targeted joint actions.

1. Write `.posecode`	2. Render the movement

| `posecode` exercise "Body-weight squat" `rig` humanoid `pose` start = standing

`step` "Descend" 1.6s settle: hips: flex 80 knees: flex 95 ankles: dorsiflex 14 ground-lock: feet cue "Sit the hips back"

`step` "Drive up" 1.2s drive: hips: flex 0 knees: flex 0 ankles: dorsiflex 0 ground-lock: feet

`repeat` 8 | |

OpenAI Build Week 2026: Posecode existed before the hackathon. During Build Week, the project was extended using Codex — running on GPT-5.6 — as the primary engineering tool for a real batch of shipped work: motion/grounding quality, language contract diagnostics, licensing restructuring, release automation, and product-facing pages. The sections below distinguish previous work from Build Week work using actual commit history, not a roadmap.

OpenAI Build Week Extension

What existed before Build Week

Before Build Week, Posecode already included:

the core .posecode domain-specific language,
a parser and intermediate motion representation,
basic range-of-motion validation,
a Three.js/WebGL renderer,
forward kinematics,
basic inverse-kinematics and ground-lock behavior,
a browser playground,
example movement files,
shareable Posecode links,
and an MCP server foundation.

This original version was developed primarily with Claude as an AI-assisted engineering tool.

That prior work provides the foundation for the project, but it is not presented as the new hackathon contribution.

What was added during Build Week

Every item below is a merged, dated pull request built with Codex (GPT-5.6) — see Build Week Evidence for direct links.

Motion and grounding overhaul — ROM-constrained reach IK, semantic palm/fist/sole/knee/pelvis contact surfaces, multi-contact refinement, stable support handoffs, and XBot-aware grounding (#76).
Language contract and diagnostics — Posecode language/IR v0.3 custom start-pose blocks with ROM-checked overrides, live and clip-wide grounding/self-collision diagnostics, and an accessible metric floor guide (#92).
Licensing restructure — split the monorepo into an Apache-2.0 standard layer (spec, parser, share, language, LSP, VS Code) and an AGPL-3.0 product layer (render, embed, MCP, eval, playground), with a commercial-license path (#84).
Release automation — Changesets-driven npm publishing via GitHub OIDC, MCP Registry publishing, and CI validation of package versions, entry points, and tarball contents (#66).
Third-party integration readiness — Posecode 0.2 timing vocabulary (drive/settle/flow/snap), a parser validation CLI, and embed compatibility metadata (#62).
Ground-lock correctness — parser-owned validation for per-side foot/hand/elbow ground locks and back-contact support for supine movements, replacing silent acceptance of invalid contacts (#61, #64).
LLM-first landing page and product page — redesigned the landing page around a prompt → Posecode → live 3D story, and added a /for-products page documenting the web component, parser, renderer, and MCP server for integrators (#82, #74).
Mobile and search fixes — mobile toolbar/viewer layout, natural hand orientation, and Google Search indexing corrections (#78, #65).

Build Week feature status

Motion/grounding quality overhaul shipped (#76)
Language contract + diagnostics shipped (#92)
Licensing restructure shipped (#84)
Release automation shipped (#66)
Ground-lock correctness shipped (#61, #64)
Landing/product pages shipped (#82, #74)

How GPT-5.6 Is Used

During Build Week, Codex sessions ran on GPT-5.6 (GPT-5.6 Terra), which is the model that powers Codex for this event. GPT-5.6 is the reasoning engine behind every Build Week change listed above: reading the existing monorepo, proposing the ROM-constrained IK and contact-surface design in #76, designing the language/IR v0.3 diagnostics in #92, and drafting the licensing boundary in #84.

A GPT-5.6-powered natural-language-to-Posecode generation feature (describe a movement in plain English, get a validated .posecode document back) is a natural next step given the existing posecode_authoring_guide MCP tool, but it is not yet built — it is not claimed as shipped functionality here.

How Codex Is Used

Codex is the primary engineering tool used for the Build Week extension.

During the hackathon period, Codex was used to:

inspect and understand the existing monorepo before each change,
design and implement the ROM-constrained reach IK and contact-surface system (#76),
design and implement the language/IR v0.3 diagnostics and floor guide (

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

Posecode MCP

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Design, images & audioi

Common questions

Alternatives

Figma Context MCP

Claude Real Video

Figma MCP Server

README from the maker

Why Posecode?

See Posecode in 28 seconds

Movement examples

Why Not Diffusion Text-to-Motion?

Resource intensive

Difficult to control

Hard to validate

Hard to debug

The Posecode Approach

The Idea in 30 Seconds

OpenAI Build Week Extension

What existed before Build Week

What was added during Build Week

Build Week feature status

How GPT-5.6 Is Used

How Codex Is Used