Mcp Server To Control Openrefine

Name: Mcp Server To Control Openrefine
Author: FiquemSabendo

@FiquemSabendo

Repository

About Mcp Server To Control Openrefine

No overview available yet

#mcp-server #openrefine #data-cleaning

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "openrefine": {
      "command": "uv",
      "args": [
        "--directory",
        "path/to/your/openrefine_mcp",
        "run",
        "openrefine_mcp/openrefine_server.py"
      ],
      "env": {
        "OPENREFINE_URL": "http://localhost:3333"
      }
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Mcp Server To Control Openrefine?

A Model Context Protocol (MCP) server that provides a typed, discoverable interface to OpenRefine’s HTTP API. It lets any MCP-capable client, such as Claude Desktop, orchestrate data-cleaning pipelines safely and reproducibly.

How to use Mcp Server To Control Openrefine?

Install Python 3.13+, uv, and ensure an OpenRefine instance is running (default: http://localhost:3333). Clone the repository, run uv sync, then configure your MCP client (e.g., Claude Desktop) by editing its configuration file to point to the server script and set the OPENREFINE_URL environment variable.

Key features of Mcp Server To Control Openrefine

Creates projects from dataset URLs
Applies editing operations to existing projects
Exports project data as CSV
Deletes projects
Provides project model information via a resource URI

Use cases of Mcp Server To Control Openrefine

Automating data cleaning steps in a reproducible pipeline
Integrating OpenRefine with AI assistants for conversational data wrangling
Applying batch operations across multiple OpenRefine projects

FAQ from Mcp Server To Control Openrefine

What is the purpose of Mcp Server To Control Openrefine?

It exposes OpenRefine's HTTP API as MCP tools and resources, enabling AI agents and other MCP clients to interact with OpenRefine programmatically.

What are the prerequisites for running the server?

Python 3.13 or higher, the uv package manager, and a running OpenRefine instance (default URL http://localhost:3333).

How do I set it up with Claude Desktop?

Create or edit the Claude Desktop configuration file to add an MCP server entry that runs uv run openrefine_mcp/openrefine_server.py from the project directory, setting the OPENREFINE_URL environment variable.

Which OpenRefine API endpoints are implemented?

The server implements create-project-from-upload, get-models, apply-operations, export-rows, and delete-project. Endpoints like set-project-metadata, set-project-tags, get-all-project-metadata, preview-expression, and get-processes are not implemented.