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Mcp Server To Control Openrefine

Mcp Server To Control Openrefine

Local
github.com/FiquemSabendoData & Analytics9Updated 23w ago

About Mcp Server To Control Openrefine

An MCP server that gives MCP clients a typed interface to OpenRefine's HTTP API. It supports project creation, operation execution, CSV export, deletion, and access to project models for data-cleaning workflows.

Key features

  1. 01Create projects from dataset URLs
  2. 02Apply operations to projects
  3. 03Export project data as CSV
  4. 04Delete projects
  5. 05Read project model information

Use cases

  1. 01Starting an OpenRefine project from a dataset URL
  2. 02Applying data-cleaning operations through an MCP client
  3. 03Exporting cleaned project data to CSV

MCP support

Exposes OpenRefine API capabilities as MCP tools and a project-model resource. MCP clients can create and manage projects, apply operations, export CSV, and inspect project structure.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

OpenRefine MCP Server

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 EndpointMCP ImplementationStatus
POST /command/core/create-project-from-uploadcreate_project(dataset_url: str, name: str | None = None)✅
GET /command/core/get-modelsget_project_models(project_id: int) resource✅
POST /command/core/apply-operationsapply_operations(project_id: int, operations: str)✅
POST /command/core/export-rowsexport_csv(project_id: int)✅
POST /command/core/delete-projectdelete_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.

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