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MCP Server To Control Openrefine

@FiquemSabendo

代码仓库

基本信息

分类

其他

传输方式

stdio

作者

FiquemSabendo

提交者

Srihari Thalla

关于 MCP Server To Control Openrefine

暂无概览

#mcp-server#openrefine#data-cleaning

MCP Server To Control Openrefine 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "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 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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

常见问题

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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