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MCP Server To Control Openrefine

@FiquemSabendo

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基本情報

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その他

トランスポート

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作者

FiquemSabendo

投稿者

Srihari Thalla

MCP Server To Control Openrefine について

概要はまだありません

#mcp-server#openrefine#data-cleaning

MCP Server To Control Openrefine の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

MCP Server To Control Openrefine のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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

よくある質問

1

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.

2

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`).

3

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.

4

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.

5

Are all OpenRefine features available through this server?

No. The server implements a subset of the OpenRefine API. The README explicitly lists several endpoints as not implemented (marked with ❌).

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