Aucterra MCP 服务器

该存储库使用aucterra-mcp包为 Aucterra 的文档理解 API 配置一个与 MCP 兼容的服务器。

它使 LLM 代理能够使用Google 的代理开发工具包 (ADK)与 Aucterra 的文档分类、提取等服务进行交互。

🔧 配置

将此块包含在您的mcpServers配置中（例如config.json或mcp.yaml ）：

{
  "mcpServers": {
    "aucterra": {
      "command": "pipx",
      "args": [
        "run",
        "--spec",
        "git+https://github.com/japisuru/aucterra-mcp",
        "aucterra-mcp"
      ],
      "env": {
        "AUCTERRA_API_KEY": "your_api_key_here"
      }
    }
  }
}

🔐 必需的环境变量

多变的	描述
`AUCTERRA_API_KEY`	用于访问 Aucterra 服务的 API 密钥

⚙️ 工具行为

此 MCP 工具提供对 Aucterra 的结构化访问：

📁 文档分类
🗂️ 键值字段提取（简单 + 列表字段）

该工具接受pdf或image文件并返回结构化的 JSON 输出。

✅ 代理集成 (Google ADK)

要在LlmAgent中使用此工具，请按如下方式配置它：

from google.adk.tools.mcp_tool.mcp_toolset import MCPToolset, StdioServerParameters

aucterra_tool = MCPToolset(
    connection_params=StdioServerParameters(
        command="pipx",
        args=[
            "run",
            "--spec",
            "git+https://github.com/japisuru/aucterra-mcp",
            "aucterra-mcp"
        ],
        env={"AUCTERRA_API_KEY": "your_api_key_here"}
    )
)

通过tools=[aucterra_tool]将此工具添加到您的代理。

📚 示例用例

User: Classify this document (/path/to/the/document/doc.pdf) into invoice or identity document.
User: Extract the following fields: NIC, Full Name, Date of Birth from this document (/path/to/the/document/doc.pdf) 
User: Extract Tax ID as 'Tax Identification Number' from this document (/path/to/the/document/doc.pdf).

代理将使用 Aucterra MCP 服务器提取字段，填充缺失值（例如，如果仅提供一个，则对field_key和field_name使用相同的值）。

📦依赖项

确保您的系统上已安装并可用pipx 。
通过以下方式安装：

python3 -m pip install --user pipx
python3 -m pipx ensurepath

🛠️ 问题

如果遇到问题，请确保您的 API 密钥正确且工具是最新的：

pipx upgrade aucterra-mcp

🔗 相关项目

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

与 MCP 兼容的服务器使 LLM 代理能够与 Aucterra 的文档理解 API 进行交互，从而提供对文档分类和字段提取服务的结构化访问。

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