DollhouseMCP

Community

Open-source MCP server for modular AI customization via personas, skills, templates, agents, and memories.

★ 39 Stars Category · Other Popular

61FMRS · C

A well-engineered MCP server for AI customization with standout security design: MCP-AQL's 5-endpoint structure cuts token usage dramatically, and the server-side Gatekeeper plus danger-zone enforcement provide permission guarantees beyond client controls. Highlights include the local-first composable element system and an active community collection; watch for third-party element vetting, incomplete Claude Desktop permission hooks, and AGPL-3.0 compliance. Extensive test coverage (9600+ tests) and solid cross-platform support.

Reliability

8/20

Security and permissions

12/20

Maintenance

12/20

Documentation

16/20

Setup experience

13/20

Read the FMRS scoring method →

DollhouseMCP is a free, open-source MCP server for dynamically customizing AI behavior with modular elements: personas, skills, templates, agents, memories, and ensembles. Elements are readable Markdown/YAML files in a local portfolio (~/.dollhouse/portfolio/), with 37 starters bundled, optional GitHub sync, and a community collection. It uses MCP-AQL, consolidating operations into 5 semantic endpoints (Create/Read/Update/Delete/Execute), and enforces element-controlled permissions through a server-side Gatekeeper: deny rules hold even when the MCP client is set to 'Always Allow'. Agent execution runs through an autonomy evaluator, danger-zone enforcement, and step audit trails. It works with Claude Code, Claude Desktop, Cursor, Gemini, Codex, VS Code, Windsurf, Cline, and LM Studio, is written in TypeScript, and is licensed AGPL-3.0.

Tools

mcp_aql_read

Read endpoint: list, search, get, activate elements, and introspect operations. Auto-approved, no side effects.

mcp_aql_create

Create endpoint: add elements, install from collection, add memory entries. Confirm once per session.

mcp_aql_update

Update endpoint: edit existing elements. Requires confirmation each time.

mcp_aql_delete

Delete endpoint: remove elements and clear entries. Requires confirmation each time.

mcp_aql_execute

Execute endpoint: run agents, manage execution lifecycle, and confirm gated operations.

convert_skill_format

Bidirectional lossless converter between Dollhouse Skills and Claude/Anthropic agent skills formats.

open_portfolio_browser

Open the built-in web portfolio browser to visually manage elements.

Setup

1) Interactive setup: run npx @dollhousemcp/mcp-server@latest --web and use the browser wizard for one-click install into Claude Desktop, Claude Code, Cursor, VS Code, Codex, Gemini CLI, Windsurf, Cline, or LM Studio; 2) Claude Code one-liner: claude mcp add -s user dollhousemcp -- npx -y @dollhousemcp/mcp-server; 3) Claude Desktop: download the .mcpb Desktop Extension and double-click; 4) Or install locally with npm install @dollhousemcp/mcp-server and point your client at dist/index.js. Configure via MCP_AQL_ENDPOINT_MODE, MCP_INTERFACE_MODE, DOLLHOUSE_PORTFOLIO_DIR, GITHUB_TOKEN.

claude_desktop_config.json

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

Generate and validate client config

Fit and risk

Best for

Advanced users who want composable, shareable elements for deep AI customization
Security-conscious teams needing permission governance and auditable agent execution
Users of MCP clients with full tool-call round-trip support, especially Claude Code

Not for

Users who require a server officially maintained by an upstream product owner (this is an independent community project)
Users relying on Claude Desktop native permission-hook flows (not supported in this release)
Beginners wanting a zero-config experience with no understanding of elements and permission policies

Required permissions

Reads and writes local Markdown/YAML element files in ~/.dollhouse/portfolio/
Optionally uses GITHUB_TOKEN for GitHub portfolio sync and community submissions
Update/delete/execute MCP-AQL operations require per-operation confirmation by default (auto-approvable via policy)
Agent execution may trigger high-risk operations (file deletion, external API calls, system commands), constrained by danger-zone enforcement and autonomy evaluation

Risks and side effects

Activating elements immediately changes AI tool access and permissions; misactivating a restrictive element can block operations
Third-party elements from the community collection may contain undesirable content or policies; review before installing
Agents can execute multi-step tasks autonomously — danger zones and audits exist, but human oversight is still required
AGPL-3.0 requires source disclosure for network use; verify compliance for commercial integrations

Troubleshooting

Verify elements are in the portfolio directory (DOLLHOUSE_PORTFOLIO_DIR) with valid YAML frontmatter
If operations are blocked, check the active element's Gatekeeper allow/deny/confirm policies — deny cannot be overridden by the client
Check MCP_AQL_ENDPOINT_MODE and MCP_INTERFACE_MODE match the desired tool exposure (crude/single/discrete)
If activations are lost after restart, check activation persistence and local file write permissions
For GitHub sync failures, verify GITHUB_TOKEN permissions; see the official Troubleshooting docs for more

Use cases

Create and activate personas, skills, templates, agents, or memories in natural language to change AI behavior

Restrict what the AI can do (e.g., a read-only analyst persona) with server-side enforcement

Run multi-step Dollhouse Agents with full audit trails and human-in-the-loop control

Back up the portfolio to GitHub, sync across machines, and contribute to the community collection

Bundle multiple elements into one activatable ensemble for one-switch workflow changes

Supported clients

Claude CodeFull support

Claude DesktopPartial support

CursorPartial support

VS CodePartial support

CodexPartial support

Gemini CLIPartial support

WindsurfPartial support

ClinePartial support

LM StudioPartial support

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