经 AI Skill Hub 精选评估,Docling MCP代理工具 获评「强烈推荐」。这款MCP工具在功能完整性、社区活跃度和易用性方面表现出色,AI 评分 8.2 分,适合有一定技术背景的用户使用。
将文档解析库Docling转化为MCP工具,赋予AI代理处理多格式文档的能力。支持PDF、Word等格式智能提取,适合需要文档智能处理的AI应用开发者和自动化工作流构建者。
Docling MCP代理工具 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
将文档解析库Docling转化为MCP工具,赋予AI代理处理多格式文档的能力。支持PDF、Word等格式智能提取,适合需要文档智能处理的AI应用开发者和自动化工作流构建者。
Docling MCP代理工具 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
# 方式一:通过 Claude Code CLI 一键安装
claude skill install https://github.com/docling-project/docling-mcp
# 方式二:手动配置 claude_desktop_config.json
{
"mcpServers": {
"docling-mcp----": {
"command": "npx",
"args": ["-y", "docling-mcp"]
}
}
}
# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
# 安装后在 Claude 对话中直接使用 # 示例: 用户: 请帮我用 Docling MCP代理工具 执行以下任务... Claude: [自动调用 Docling MCP代理工具 MCP 工具处理请求] # 查看可用工具列表 # 在 Claude 中输入:"列出所有可用的 MCP 工具"
// claude_desktop_config.json 配置示例
{
"mcpServers": {
"docling_mcp____": {
"command": "npx",
"args": ["-y", "docling-mcp"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
<p align="center"> <a href="https://github.com/docling-project/docling-mcp"> <img loading="lazy" alt="Docling" src="https://github.com/docling-project/docling-mcp/raw/main/docs/assets/docling_mcp.png" width="40%"/> </a> </p>
Docling MCP is a service that provides tools for document conversion, processing and generation. It uses the Docling library to convert PDF documents into structured formats and provides a caching mechanism to improve performance. The service exposes functionality through a set of tools that can be called by client applications.
For users who need local conversion or don't have Docling Serve access:
pip install docling-mcp[local]
Then configure your environment:
export DOCLING_MCP_CONVERSION_MODE=local
| Variable | Default | Description |
|---|---|---|
DOCLING_MCP_SERVICE_URL | — | URL of the Docling Serve instance |
DOCLING_MCP_SERVICE_API_KEY | — | API key for the service |
DOCLING_MCP_SERVICE_TIMEOUT | 300.0 | Timeout in seconds for a whole conversion job |
DOCLING_MCP_SERVICE_MAX_RETRIES | 3 | Max retry attempts |
DOCLING_MCP_FALLBACK_TO_LOCAL | false | Fall back to local if service is unreachable (requires docling-mcp[local]) |
The easiest way to install Docling MCP and connect it to your client is by launching it via uvx.
Depending on the transfer protocol required, specify the argument --transport, for example
stdio used e.g. in Claude for Desktop and LM Studio uvx --from docling-mcp docling-mcp-server --transport stdio
sse used e.g. in Llama Stack uvx --from docling-mcp docling-mcp-server --transport sse
streamable-http used e.g. in containers setup uvx --from docling-mcp docling-mcp-server --transport streamable-http
More options are available, e.g. the selection of which toolgroup to launch. Use the --help argument to inspect all the CLI options.
For developing the MCP tools further, please refer to the Developing section of CONTRIBUTING.md for instructions.
All settings use the DOCLING_MCP_ prefix and can be supplied as environment variables, in a .env file in the working directory, or via the env block of your MCP client config. Copy .env.example as a starting point.
{
"mcpServers": {
"docling": {
"command": "uvx",
"args": [
"--from=docling-mcp",
"docling-mcp-server"
],
"env": {
"DOCLING_MCP_CONVERSION_MODE": "remote",
"DOCLING_MCP_SERVICE_URL": "https://your-docling-service.example.com",
"DOCLING_MCP_SERVICE_API_KEY": "your-api-key-here"
}
}
}
}
| Variable | Default | Description |
|---|---|---|
DOCLING_MCP_KEEP_IMAGES | false | Retain page images in output |
DOCLING_MCP_IMAGES_SCALE | 1.0 | Image scale factor (increase to avoid tensor padding errors) |
DOCLING_MCP_DO_OCR | true | Run OCR pipeline |
DOCLING_MCP_DO_TABLE_STRUCTURE | true | Detect table structure |
One of the easiest ways to experiment with the tools provided by Docling MCP is to leverage an AI desktop client with MCP support. Most of these clients use a common config interface. Adding Docling MCP in your favorite client is usually as simple as adding the following entry in the configuration file.
{
"mcpServers": {
"docling": {
"command": "uvx",
"args": [
"--from=docling-mcp",
"docling-mcp-server"
]
}
}
}
When using Claude for Desktop, simply edit the config file claude_desktop_config.json with the snippet above or the example provided here.
In LM Studio, edit the mcp.json file with the appropriate section or simply click on the button below for a direct install.
Other integrations are described in the [integrations] page.
优质MCP工具,将成熟文档处理能力代理化,填补AI应用文档理解空白,维护活跃,值得关注。
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。
AI Skill Hub 点评:Docling MCP代理工具 的核心功能完整,质量优秀。对于Claude Desktop / Claude Code 用户来说,这是一个值得纳入个人工具库的选择。建议先在非生产环境试用,再逐步推广。
| 原始名称 | docling-mcp |
| 原始描述 | 开源MCP工具:Making docling agentic through MCP。⭐619 · Python |
| Topics | 文档处理MCP工具AI代理Python |
| GitHub | https://github.com/docling-project/docling-mcp |
| License | MIT |
| 语言 | Python |
收录时间:2026-05-19 · 更新时间:2026-05-30 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。
选择 Agent 类型,复制安装指令后粘贴到对应客户端