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OpenRefine Server

БесплатноНе проверен

Enables MCP-capable clients to interact with OpenRefine's HTTP API for creating projects, applying operations, exporting CSV, and deleting projects.

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Описание

Enables MCP-capable clients to interact with OpenRefine's HTTP API for creating projects, applying operations, exporting CSV, and deleting projects.

README

Test

A Model Context Protocol (MCP) server that provides a typed, discoverable interface to OpenRefine's HTTP API. This allows any MCP-capable client (like Claude Desktop) to orchestrate data-cleaning pipelines safely and reproducibly.

Installation

Prerequisites

  • Python 3.13 or higher
  • uv package manager
  • OpenRefine instance running (default: http://localhost:3333)

Install the Package

# Clone the repository
git clone <repository-url>
cd openrefine_mcp

# Install dependencies using uv
uv sync

Setup Claude Desktop

  1. Create or edit your Claude Desktop configuration file:

    # On macOS/Linux
    ~/.config/claude_desktop_config.json
    
    # On Windows
    %APPDATA%\claude_desktop_config.json
    
  2. Add the OpenRefine MCP server to your configuration:

    {
      "mcpServers": {
        "openrefine": {
          "command": "uv",
          "args": [
            "--directory",
            "path/to/your/openrefine_mcp",
            "run",
            "openrefine-mcp-server"
          ],
          "env": {
            "OPENREFINE_URL": "http://localhost:3333"
          }
        }
      }
    }
    
  3. Restart Claude Desktop to load the new MCP server.

Features

This MCP server implements the following OpenRefine API endpoints:

OpenRefine API Endpoint MCP Implementation Status
POST /command/core/create-project-from-upload create_project(dataset_url: str, name: str | None = None)
GET /command/core/get-models get_project_models(project_id: int) resource
POST /command/core/apply-operations apply_operations(project_id: int, operations: str)
POST /command/core/export-rows export_csv(project_id: int)
POST /command/core/delete-project delete_project(project_id: int)
POST /command/core/set-project-metadata -
POST /command/core/set-project-tags -
GET /command/core/get-all-project-metadata -
POST /command/core/preview-expression -
GET /command/core/get-processes -

Available Tools

  • create_project(dataset_url: str, name: str | None = None) → Creates a new OpenRefine project from a dataset URL
  • apply_operations(project_id: int, operations: str) → Applies operations to an OpenRefine project
  • export_csv(project_id: int) → Exports CSV data from an OpenRefine project
  • delete_project(project_id: int) → Deletes an OpenRefine project

Available Resources

  • openrefine://project/{project_id}/models → Returns structural information about the project including column definitions, record model configuration, available scripting languages, and overlay models

Development

Running Tests

make test

Running the MCP Inspector server

make inspector

License

This project is licensed under the MIT License - see the LICENSE file for details.

from github.com/FiquemSabendo/openrefine_mcp

Установка OpenRefine Server

У этого сервера нет опубликованного пакета — он собирается из исходников. Открой репозиторий и следуй инструкции в README.

▸ github.com/FiquemSabendo/openrefine_mcp

FAQ

OpenRefine Server MCP бесплатный?

Да, OpenRefine Server MCP бесплатный — установка в пару кликов через Unyly без оплаты.

Нужен ли API-ключ для OpenRefine Server?

Нет, OpenRefine Server работает без API-ключей и переменных окружения.

OpenRefine Server — hosted или self-hosted?

Self-hosted: сервер запускается локально на твоей машине командой из раздела установки.

Как установить OpenRefine Server в Claude Desktop, Claude Code или Cursor?

Открой OpenRefine Server на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.

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