DollhouseMCP

Name: DollhouseMCP
Author: DollhouseMCP

by DollhouseMCPversion 2.0.43i★ –iRuns on your computer

Registry nameio.github.DollhouseMCP/mcp-server

Install

Overview

Description from the maker (original English)

OSS to create Personas, Skills, Templates, Agents, and Memories to customize your AI experience.

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: Other

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).
The registry lists no key to fill in.

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": {
    "dollhousemcp": {
      "command": "npx",
      "args": [
        "-y",
        "@dollhousemcp/mcp-server"
      ]
    }
  }
}

Run this in your terminal

claude mcp add dollhousemcp -- npx -y @dollhousemcp/mcp-server

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "dollhousemcp": {
      "command": "npx",
      "args": [
        "-y",
        "@dollhousemcp/mcp-server"
      ]
    }
  }
}

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": {
    "dollhousemcp": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@dollhousemcp/mcp-server"
      ]
    }
  }
}

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.

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 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account DollhouseMCP, 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 Otheri

More stars than 0% of the other 18333 servers in this category
Published to the registry more recently than 61% of the other 18333 servers in this category
Basic checks equal to or better than 100% of the other 18333 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 DollhouseMCP 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 (2.0.43) reached the registry 29 days 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: Spec Driven Development, Remote Desktop Commander, Signoff

Alternatives

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

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Runs on your computer★ 440MITUpdated 14 months ago

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Remote Desktop Commander

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Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

Runs in the cloud★ 211Updated 2 days ago

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Signoff

by ayuanxx-max

Five gates that make an agent's 'done' provable: run them, read the verdict, audit the record.

Runs on your computerUpdated today

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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.

DollhouseMCP

Open-source AI customization through modular elements.

Website · Browse the Collection · NPM Package · Discord

How It Works

 CREATE or EDIT                    PORTFOLIO                   ACTIVATE → USE

─────────────────────────────────────────────────────────────────────────────

"Create a skill for 📁 ~/.dollhouse/portfolio/ "Activate the Dollhouse writing blog posts" Expert ensemble" 37 starter elements: "Edit the code review ──▶ personas · skills · ──▶ Your AI now has persona to add security" templates · agents · new behavior, memories · ensembles capabilities, and persona · skill · template permission policies agent · memory · ensemble + everything you create

community installs

Pick any path to start:

Activate a starter element from your portfolio — your AI immediately changes
Create any element type (persona, skill, template, agent, memory, ensemble) by describing what you want in plain English
Edit any existing element to refine it
Browse the community collection and install elements made by other users

Your portfolio (~/.dollhouse/portfolio/ on macOS/Linux, %USERPROFILE%\.dollhouse\portfolio\ on Windows) is a local folder that holds all your Dollhouse elements. It ships with 37 starters — including the dollhouse-expert-suite ensemble (persona + knowledge base) you can activate for guided help. Everything you create or install lands here. Share back to the community or sync to GitHub whenever you're ready.

Quick Start

v2.0.0 is now available. DollhouseMCP v2 is the default release. Release notes | Migration guide | Report issues

DollhouseMCP installs on any MCP-compatible AI client — Claude Code, Claude Desktop, Cursor, Gemini, Codex, and local LLMs. Core element management (create, activate, search, browse) works across all platforms. Advanced features (Gatekeeper confirmation flows, agentic loop execution) have been tested extensively on Claude Code and should work on any client that supports standard MCP tool call/response patterns.

Interactive Setup (any platform):

npx @dollhousemcp/mcp-server@latest --web

Opens a browser-based setup wizard with one-click install for Claude Desktop, Claude Code, Cursor, VS Code, Codex, Gemini CLI, Windsurf, Cline, and LM Studio. Detects existing installations, supports auto-updating and pinned versions.

Permission hook support in the setup wizard currently breaks down like this:

Platform	Setup status	Permission hook status
Claude Code	One-click install	Full native support
Cursor	One-click install	Partial native support
VS Code	One-click install	Partial native support
Codex	One-click install	Partial native support, Bash-only
Gemini CLI	One-click install	Partial native support
Windsurf	One-click install	Partial native support
Cline	One-click install	MCP setup only in this release
LM Studio	One-click install	MCP setup only in this release
Claude Desktop	One-click install	No native permission hook path in this release

Claude Code (one command):

claude mcp add -s user dollhousemcp -- npx -y @dollhousemcp/mcp-server

Claude Desktop (one-click install):

Download the DollhouseMCP Desktop Extension (.mcpb file) and double-click it. Claude Desktop handles the rest — no terminal required.

Other platforms — see the Quick Start Guide or run the interactive setup above.

Then start a conversation:

"What DollhouseMCP tools do you have available?"

"List all available Dollhouse personas" "Activate the Dollhouse debug detective persona"

DollhouseMCP ships with 38 Dollhouse elements across all 6 types. Just describe what you want in natural language.

First time? The Public Beta Onboarding Guide walks you from install to your first activated Dollhouse persona in under 10 minutes.

Dollhouse Elements: Behavior, Capabilities, and Permissions

Dollhouse elements are modular building blocks that customize your AI. When you activate a Dollhouse element, you're not just changing a prompt — you're changing what tools the AI can access, what commands it can run, and what operations require your approval.

Dollhouse Element	What It Does

| Dollhouse Personas | Shape behavior, tone, expertise, and priorities. Act as security principals with permission policies that control what the AI can do. | | Dollhouse Skills* | Add discrete capabilities the AI can activate on demand. Code review, data analysis, penetration testing, translation, and more. | | Dollhouse Templates | Standardize outputs with variable substitution. Reports, emails, briefs, documentation — consistent structure every time. Variables are auto-derived from {{placeholder}} tokens in content — no manual schema needed. | | Dollhouse Agents | Execute multi-step goals autonomously. State tracking, resilience policies, autonomy evaluation, and an execution lifecycle. | | Dollhouse Memories | Persist structured context across sessions. Facts, preferences, project state. Can auto-load on startup. | | Dollhouse Ensembles | Bundle multiple elements into one activatable unit. Activation strategies, conflict resolution, and coordinated permission policies. |

**\*Skills Compatibility**

Dollhouse Skills (introduced July 2025) predate the agent skills format later adopted by Claude/Anthropic. DollhouseMCP includes a built-in lossless bidirectional converter between the two formats.

- Import: Convert agent skills → Dollhouse Skills via convert_skill_format. Once converted, they're first-class Dollhouse elements — combinable with Personas, Templates, and other Skills inside Ensembles, managed by Dollhouse Agents, and protected by Gatekeeper policies.

- Export: Convert Dollhouse Skills → agent skills for platforms that don't have DollhouseMCP installed.

- Roundtrip: The converter supports a lossless mode that preserves everything in both directions. A safe mode is also available that sanitizes potentially risky patterns during conversion.

Full Skills Converter documentation

All Dollhouse elements are readable markdown or YAML files stored in your local portfolio. You own them, you control them. When interacting with your AI, use "Dollhouse" to disambiguate — say "activate the Dollhouse code review persona" or "run the Dollhouse research agent" to ensure the AI uses DollhouseMCP elements rather than native platform features.

MCP-AQL: How Your AI Talks to DollhouseMCP

Most MCP servers expose dozens of individual tools, each consuming context tokens and forcing the LLM to pick the right one from a flat list. DollhouseMCP takes a different approach.

MCP-AQL (Model Context Protocol – Advanced Agent API Adapter Query Language) organizes all operations into 5 semantic endpoints — CRUDE: Create, Read, Update, Delete, Execute. The A pulls quadruple duty: Advanced query capabilities, Agent-first design, API consolidation, and Adapter layer to bridge other MCP servers and APIs to work directly with LLMs. Each endpoint groups operations by what they do to state, giving the LLM clear semantic signals about the consequences of each action:

Endpoint	Purpose	Permission Level
Create	Add new elements, install from collection, add memory entries	Confirm once per session
Read	List, search, get details, activate, introspect	Auto-approved (safe, no side effects)
Update	Edit existing elements	Confirm each time
Delete	Remove elements, clear entries	Confirm each time
Execute	Run agents, manage execution lifecycle, confirm operations	Confirm each time

Why This Matters

Semantic clarity — The LLM knows that calling mcp_aql_read is always safe. Calling mcp_aql_delete is always destructive. No guessing.
Host-level permission control — MCP clients like Claude Code can set different approval policies per endpoint (auto-approve reads, require confirmation for deletes).
Progressive disclosure through introspection — The LLM starts with just 5 tool endpoints. It discovers operations, parameters, element formats, and usage examples at runtime by asking the server:

  { "operation": "introspect", "params": { "query": "operations" } }

{ "operation": "introspect", "params": { "query": "format", "name": "persona" } }

This is progressive disclosure built into the protocol — the LLM only loads what it needs, when it needs it. Unlike client-side solutions that require special harness support (like Claude Code's deferred tool loading), MCP-AQL's introspection works on any MCP client because it's just a standard tool call that returns structured data. No fancy client features required. The server describes itself.

Eleme

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

DollhouseMCP

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

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 Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

Signoff

README from the maker

DollhouseMCP

How It Works

Quick Start

Dollhouse Elements: Behavior, Capabilities, and Permissions

MCP-AQL: How Your AI Talks to DollhouseMCP

Why This Matters