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Ragie Model Context Protocol Server

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Ragie 模型上下文协议服务器

提供对 Ragie 知识库检索功能的访问的模型上下文协议 (MCP) 服务器。

描述

该服务器实现了模型上下文协议 (MCP)，使 AI 模型能够从 Ragie 知识库中检索信息。它提供了一个名为“检索”的工具，允许查询知识库中的相关信息。

Related MCP server: RAG Information Retriever

先决条件

  • Node.js >= 18

  • Ragie API 密钥

安装

服务器需要以下环境变量：

  • RAGIE_API_KEY （必需）：您的 Ragie API 身份验证密钥

服务器将启动并在 stdio 上监听 MCP 协议消息。

使用 npx 安装并运行服务器：

RAGIE_API_KEY=your_api_key npx @ragieai/mcp-server

命令行选项

服务器支持以下命令行选项：

  • --description, -d <text> ：使用自定义文本覆盖默认工具描述

  • --partition, -p <id> : 指定要查询的 Ragie 分区 ID

例子：

# With custom description
RAGIE_API_KEY=your_api_key npx @ragieai/mcp-server --description "Search the company knowledge base for information"

# With partition specified
RAGIE_API_KEY=your_api_key npx @ragieai/mcp-server --partition your_partition_id

# Using both options
RAGIE_API_KEY=your_api_key npx @ragieai/mcp-server --description "Search the company knowledge base" --partition your_partition_id

游标配置

要将此 MCP 服务器与 Cursor 一起使用：

选项 1：创建 MCP 配置文件

  1. 保存一个名为mcp.json的文件

  • 对于特定于项目的工具，请在项目目录中创建一个.cursor/mcp.json文件。这允许您定义仅在该特定项目中可用的 MCP 服务器。

  • 对于想要在所有项目中使用的工具，请在主目录中创建一个~/.cursor/mcp.json文件。这样，MCP 服务器就可以在所有 Cursor 工作区中使用。

示例mcp.json ：

{
  "mcpServers": {
    "ragie": {
      "command": "npx",
      "args": [
        "-y",
        "@ragieai/mcp-server",
        "--partition",
        "optional_partition_id"
      ],
      "env": {
        "RAGIE_API_KEY": "your_api_key"
      }
    }
  }
}

选项 2：使用 shell 脚本

  1. 在您的系统上保存一个名为ragie-mcp.sh的文件：

#!/usr/bin/env bash

export RAGIE_API_KEY="your_api_key"

npx -y @ragieai/mcp-server --partition optional_partition_id

  1. 授予文件执行权限： chmod +x ragie-mcp.sh

  2. 通过转到 Cursor UI 中的**“设置”** -> “Cursor 设置” -> **“MCP 服务器”**来添加 MCP 服务器脚本。

用您的实际 Ragie API 密钥替换your_api_key ，并根据需要选择设置分区 ID。

Claude桌面配置

要将此 MCP 服务器与 Claude 桌面一起使用：

  1. 创建 MCP 配置文件claude_desktop_config.json ：

  • 对于 MacOS：使用~/Library/Application Support/Claude/claude_desktop_config.json

  • 对于 Windows：使用%APPDATA%/Claude/claude_desktop_config.json

示例claude_desktop_config.json ：

{
  "mcpServers": {
    "ragie": {
      "command": "npx",
      "args": [
        "-y",
        "@ragieai/mcp-server",
        "--partition",
        "optional_partition_id"
      ],
      "env": {
        "RAGIE_API_KEY": "your_api_key"
      }
    }
  }
}

用您的实际 Ragie API 密钥替换your_api_key ，并根据需要选择设置分区 ID。

  1. 重新启动 Claude 桌面以使更改生效。

Ragie 检索工具现在可在您的 Claude 桌面对话中使用。

特征

检索工具

服务器提供了一个retrieve工具，可用于搜索知识库。它接受以下参数：

  • query （字符串）：用于查找相关信息的搜索查询

该工具返回：

  • 包含来自知识库的匹配文本的内容块数组

发展

该项目使用 TypeScript 编写，并使用以下主要依赖项：

  • @modelcontextprotocol/sdk ：用于实现 MCP 服务器

  • ragie ：用于与 Ragie API 交互

  • zod ：用于运行时类型验证

开发设置

以开发模式运行服务器：

RAGIE_API_KEY=your_api_key npm run dev -- --partition optional_partition_id

构建项目：

npm run build

执照

MIT 许可证 - 详情请参阅 LICENSE.txt。

Available Tools

1 tool
retrieveA

Look up information in the Knowledge Base. Use this tool when you need to:

  • Find relevant documents or information on specific topics

  • Retrieve company policies, procedures, or guidelines

  • Access product specifications or technical documentation

  • Get contextual information to answer company-specific questions

  • Find historical data or information about projects

ParametersJSON Schema
NameRequiredDescriptionDefault
topKNoThe maximum number of results to return. Defaults to 8.
queryYesThe query to search for data in the Knowledge Base
filterNoThe metadata search filter on documents. Returns chunks only from documents which match the filter. The following filter operators are supported: $eq - Equal to (number, string, boolean), $ne - Not equal to (number, string, boolean), $gt - Greater than (number), $gte - Greater than or equal to (number), $lt - Less than (number), $lte - Less than or equal to (number), $in - In array (string or number), $nin - Not in array (string or number). The operators can be combined with AND and OR. Read Metadata & Filters guide for more details and examples.
rerankNoWhether to try and find only the most relevant data. Defaults to false.
recencyBiasNoWhether to favor data towards more recent documents. Defaults to false.

TDQS

A3.5/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must carry the full burden. It only implies a read-only operation by saying 'Look up information', but fails to explicitly state it is read-only, does not disclose authentication needs, rate limits, or error behavior. This is a significant gap for a retrieval tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is relatively concise, using a bullet list of use cases. It is front-loaded with the purpose statement. However, some redundancy exists with 'Use this tool when you need to' repeated for each item.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has 5 parameters, including a complex nested filter object, and no output schema. The description does not explain the return format, pagination, or how results are structured. It only vaguely mentions 'information', leaving the agent without sufficient context to interpret the response.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and all parameters have descriptions in the schema. The tool description does not add additional meaning beyond what the schema provides. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Look up information in the Knowledge Base' and lists specific use cases (e.g., 'Find relevant documents', 'Retrieve company policies'). It directly addresses what the tool does with a specific verb and resource.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a bullet list of when to use the tool, such as 'Find relevant documents or information' and 'Get contextual information'. It implicitly guides usage but does not explicitly state when not to use or mention alternatives, though no sibling tools exist.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.2/5.0
Disambiguation5/5

With only one tool, there is no potential for confusion between tools. The tool's purpose is clearly defined.

Naming Consistency3/5

With a single tool named 'retrieve', there is no pattern to evaluate. Naming is neither consistent nor inconsistent—it's neutral.

Tool Count1/5

A knowledge base server with only one retrieval tool is extremely minimal. Agents cannot perform any CRUD operations, making this count far too low for the implied scope.

Completeness1/5

The server only supports retrieval. Essential actions like adding, updating, or deleting documents are missing, leaving significant gaps in functionality.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Unclaimed servers have limited discoverability.

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