jamovi MCP

English | 简体中文

A local stdio MCP server that lets Claude, Cursor, and other MCP clients control jamovi.

Open datasets, inspect schemas, edit cells, run statistical analyses, export results, and save .omv files through a local jamovi engine.

Fastest Setup

Copy this into your MCP client config. Claude Code, Claude Desktop, Cursor, and most stdio MCP clients use this command + args shape:

{
  "mcpServers": {
    "jamovi": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/yjm110517/jamovi-mcp.git",
        "jamovi-mcp"
      ]
    }
  }
}

If you use Claude Code, do not add "type": "stdio" unless your client documentation explicitly requires it, and do not copy a placeholder JAMOVI_HOME value into your config. Start with the minimal config above.

Optional check:

uvx --from git+https://github.com/yjm110517/jamovi-mcp.git -- jamovi-mcp --check

After it prints jamovi: ... and MCP transport: stdio, restart your MCP client, then call jamovi_open with an absolute local data file path.

This is the recommended setup for normal users. You do not need to clone this repository, install a local lib/ directory, or hardcode a machine-specific Python path.

Requirements

• Windows

• jamovi installed locally

• uvx available to the MCP client

• A Python 3.10+ runtime. uvx provisions this automatically — no manual Python install needed.

uvx is part of uv, a Python tool runner. In this README it is used so your MCP client can download and run jamovi-mcp from GitHub without cloning the repository or hardcoding a local Python path.

Install uv on Windows:

winget install astral-sh.uv

If your MCP client still cannot find uvx after installation, restart the MCP client and, if needed, restart the terminal or Windows session so PATH changes are visible.

jamovi itself is required because this MCP starts a local jamovi engine process. Python does not need to be installed in any specific directory.

jamovi Version Discovery

No configuration required in most cases. The server scans all fixed drives:

1. *\Program Files\jamovi* and *\Program Files (x86)\jamovi* on every drive

2. *\jamovi* at each drive root (e.g. D:\jamovi 2.6)

3. *\*\jamovi* one level below each drive root (e.g. D:\Tools\jamovi)

The newest valid installation is selected automatically.

When auto-discovery cannot find jamovi

If jamovi is installed deep inside a custom folder tree, set JAMOVI_HOME in the MCP config:

{
  "mcpServers": {
    "jamovi": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/yjm110517/jamovi-mcp.git",
        "jamovi-mcp"
      ],
      "env": {
        "JAMOVI_HOME": "D:\\MyTools\\jamovi"
      }
    }
  }
}

JAMOVI_HOME must point to the directory containing Frameworks and Resources. To verify you have the right path, run the directory listing and confirm you see both folders:

dir "D:\MyTools\jamovi"
# Should show: Frameworks\  Resources\  bin\  ...

Example Workflow

You can ask your MCP client to:

Open survey.csv, show the variables, read the first 10 rows, run a t-test, and save the project as analysis.omv.

Typical tool sequence:

1. jamovi_open

2. jamovi_get_schema

3. jamovi_get_data

4. jamovi_run_analysis

5. jamovi_save

MCP Tools

This server exposes 10 MCP tools.

Usage Examples

Open a CSV file:

{
  "file_path": "C:\\Users\\you\\data\\example.csv"
}

Read the active dataset schema:

{}

Read the first 10 rows and first 3 columns:

{
  "row_start": 0,
  "row_count": 10,
  "column_start": 0,
  "column_count": 3
}

Set a single cell value:

{
  "row": 0,
  "column": 1,
  "value": 10
}

Save the active dataset:

{
  "file_path": "C:\\Users\\you\\data\\output.omv",
  "overwrite": true
}

Run an analysis:

{
  "ns": "jmv",
  "name": "ttestIS",
  "options": {
    "vars": ["score"],
    "students": true
  },
  "analysis_id": 2
}

Architecture

flowchart LR
    Client["MCP Client"] --> Stdio["stdio MCP transport"]
    Stdio --> Server["jamovi_mcp.server"]

    Server --> ToolMap["Tool dispatcher"]
    ToolMap --> FileTools["tools.files"]
    ToolMap --> DataTools["tools.data"]
    ToolMap --> AnalysisTools["tools.analysis"]

    FileTools --> Connection["JamoviConnection"]
    DataTools --> Connection
    AnalysisTools --> Connection

    Server --> Engine["EngineManager"]
    Engine --> Config["config.py"]
    Config --> Discovery["JAMOVI_HOME or Program Files discovery"]
    Config --> EnvConf["bin/env.conf parsing"]
    Discovery --> JamoviInstall["Local jamovi installation"]
    EnvConf --> JamoviInstall

    Engine --> JamoviServer["jamovi.server subprocess"]
    JamoviInstall --> JamoviServer

    Connection --> HTTP["HTTP open/save endpoints"]
    Connection --> WS["WebSocket + protobuf coms"]
    HTTP --> JamoviServer
    WS --> JamoviServer

    AnalysisTools --> Registry["analyses.py registry"]
    Registry --> Modules["Resources/modules YAML"]
    Modules --> JamoviInstall

At startup, EngineManager selects a jamovi installation through config.py, builds the process environment from jamovi's own bin/env.conf, and launches jamovi.server. The MCP server connects to that local engine through JamoviConnection. File operations use jamovi's HTTP routes, while dataset and analysis operations use WebSocket messages encoded with the bundled protobuf definitions.

Compatibility

Verified locally:

• Windows

• Python 3.10 - 3.12

• jamovi 2.6.19.0

Designed compatibility:

• Any Python 3.10+ runtime (the minimum is enforced by the MCP SDK).

• Optional version selection through JAMOVI_HOME.

• Automatic newest-version selection when multiple jamovi* directories are installed under standard Program Files locations.

Known limitation:

• If a future jamovi release changes jamovi.proto, the WebSocket request types, or the HTTP open/save routes, this MCP may need an adapter update and regenerated protobuf code.

Troubleshooting

uvx is not found

Install uv so your MCP client can run uvx, then restart the MCP client:

winget install astral-sh.uv

uvx means "run a Python tool through uv". If you do not want to use uvx, use the development install below and configure the installed jamovi-mcp command instead.

jamovi-mcp requires Python 3.10 or newer

Your MCP client is using an older Python runtime. With uvx, uv automatically provisions the correct Python version — no manual setup required. If you manage Python yourself, point the MCP command to a Python 3.10+ executable:

{
  "command": "C:\\Path\\To\\Python\\python.exe",
  "args": ["-m", "jamovi_mcp"]
}

This is an advanced fallback. It is not the recommended setup and the path will differ on every computer.

Invalid JAMOVI_HOME

JAMOVI_HOME must point to the jamovi installation directory that contains Frameworks and Resources.

Example:

{
  "env": {
    "JAMOVI_HOME": "C:\\Your\\jamovi\\Install\\Path"
  }
}

Do not copy C:\\Your\\jamovi\\Install\\Path literally. It is a placeholder. Replace it with a real path, or remove the entire env block when jamovi is installed under a standard Program Files location.

jamovi is installed but not detected

Set JAMOVI_HOME explicitly in the MCP client config. This is also recommended when testing a specific jamovi version.

Claude Code reports an MCP config schema error

Start with the minimal config and keep only command and args:

{
  "mcpServers": {
    "jamovi": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/yjm110517/jamovi-mcp.git",
        "jamovi-mcp"
      ]
    }
  }
}

The most common causes are extra fields unsupported by the current client, or copying a placeholder JAMOVI_HOME value into the config. Get the minimal config working first, then add a real env block only if needed.

File open or save fails

Use absolute Windows paths and make sure the user running the MCP client has permission to read or write that location. For save operations, pass "overwrite": true if the target file already exists.

Analysis tools return unexpected results

First call jamovi_list_analyses, then jamovi_get_analysis_options for the target analysis. jamovi analysis option schemas are module-specific and can differ between versions or installed modules.

Development Install

Normal users should use the uvx MCP config above. Clone the repository only if you want to develop or test the code locally.

git clone https://github.com/yjm110517/jamovi-mcp.git
cd jamovi-mcp
py -3.10 -m pip install -e .

If your system does not have the Windows Python launcher, use any Python 3.10+ executable instead:

python -m pip install -e .

Run tests:

py -3.10 -m pytest -q

Start the MCP server directly:

py -3.10 -m jamovi_mcp

Important source areas:

• src/jamovi_mcp/server.py: MCP server and tool registration.

• src/jamovi_mcp/engine.py: jamovi engine subprocess lifecycle.

• src/jamovi_mcp/config.py: jamovi install discovery and environment setup.

• src/jamovi_mcp/connection.py: HTTP, WebSocket, and protobuf communication.

• src/jamovi_mcp/tools/: MCP tool implementations.

• src/jamovi_mcp/analyses.py: analysis registry built from jamovi module YAML files.

• tests/: unit tests for data conversion, save handling, config, and engine env setup.

Do not commit lib/ or other local dependency target directories. Install dependencies through pyproject.toml.

Security Notes

This MCP starts a local jamovi process and reads or writes local files whose paths are provided through MCP tool calls.

• The engine is started locally and connected through 127.0.0.1.

• File paths are supplied by the MCP client/user.

• Do not expose this server to untrusted clients.

• Do not pass sensitive data files to an MCP client you do not trust.

• Do not commit private local config, access tokens, API keys, or datasets.

Roadmap

• Add GitHub Actions CI.

• Add broader integration tests across more jamovi versions.

• Improve structured parsing for analysis result payloads.

• Add more explicit typed response schemas for each MCP tool.

• Document common jamovi analysis recipes.

Contributing

Pull requests are welcome. Please keep changes focused, run the test suite before submitting, and include tests for behavior changes.

For compatibility work, include the jamovi version, Windows version, and Python version used for testing.

Repository Contents

Files that should be committed:

• README.md

• README.zh-CN.md

• LICENSE

• .gitignore

• pyproject.toml

• docs/

• src/

• tests/

Files and directories that should not be committed:

• lib/

• .pytest_cache/

• .ruff_cache/

• __pycache__/

• local CSV/OMV/log/tmp files

• private local config, tokens, and API keys

License

MIT