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Ragie Model Context Protocol Server: Workflow Mirroring & Precision - MCP Implementation

Ragie Model Context Protocol Server: Workflow Mirroring & Precision

Ragie Model Context Protocol Server: Seamlessly mirror complex workflows with pinpoint accuracy, boosting efficiency for teams that demand precision. Mirror of excellence.

Research And Data
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Ranked in the top 7% of all AI tools in its category

About Ragie Model Context Protocol Server

What is Ragie Model Context Protocol Server: Workflow Mirroring & Precision?

Ragie Model Context Protocol (MCP) Server acts as an interface for AI models to access Ragie’s knowledge base. By implementing the MCP standard, it provides a "retrieve" tool enabling precise information retrieval through structured queries. This server mirrors workflow processes to ensure accurate data extraction while maintaining contextual precision, making it essential for integrating domain-specific knowledge into AI workflows.

How to Use Ragie Model Context Protocol Server: Workflow Mirroring & Precision?

Getting started involves three core steps:
1. Setup: Install dependencies and configure environment variables (e.g., RAGIE_API_KEY).
2. Deployment: Run the server with CLI options like --partition for dataset targeting.
3. Integration: Use the "retrieve" API in your application with parameters like query strings and topK limits. For platform-specific setups, follow guides for Cursor or Claude integration.

Ragie Model Context Protocol Server Features

Key Features of Ragie Model Context Protocol Server: Workflow Mirroring & Precision?

  • Adaptive Query Processing: Supports reranking and recency bias toggles to refine result relevance.
  • Flexible Output Control: Adjust topK (default 8) to balance result volume and performance.
  • API Extensibility: Built with TypeScript and battle-tested libraries like Zod for robust validation.
  • Multi-Environment Support: Works across development (via npm run dev) and production builds.

Use Cases of Ragie Model Context Protocol Server: Workflow Mirroring & Precision?

Organizations leverage this server for:
• Enterprise knowledge management systems requiring versioned document tracking.
• Real-time legal research tools prioritizing recent case law updates.
• Customer service bots needing context-aware product database queries.

Ragie Model Context Protocol Server FAQ

FAQ from Ragie Model Context Protocol Server: Workflow Mirroring & Precision?

  • Q: Can I use this with non-MCP frameworks?
    A: Yes, but MCP integration ensures optimal performance and compatibility.
  • Q: How does recency bias affect results?
    A: Enabled via recencyBias: true, it weights newer documents higher without excluding historical data.
  • Q: What’s the maximum query length?
    A: 512 tokens recommended; longer inputs may reduce reranking accuracy.

Content

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Ragie Model Context Protocol Server

A Model Context Protocol (MCP) server that provides access to Ragie's knowledge base retrieval capabilities.

Description

This server implements the Model Context Protocol to enable AI models to retrieve information from a Ragie knowledge base. It provides a single tool called "retrieve" that allows querying the knowledge base for relevant information.

Prerequisites

  • Node.js >= 18
  • A Ragie API key

Installation

The server requires the following environment variable:

  • RAGIE_API_KEY (required): Your Ragie API authentication key

The server will start and listen on stdio for MCP protocol messages.

Install and run the server with npx:

RAGIE_API_KEY=your_api_key npx @ragieai/mcp-server

Command Line Options

The server supports the following command line options:

  • --description, -d <text>: Override the default tool description with custom text
  • --partition, -p <id>: Specify the Ragie partition ID to query

Examples:

# 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

Cursor Configuration

To use this MCP server with Cursor:

Option 1: Create an MCP configuration file

  1. Save a file called mcp.json
  • For tools specific to a project , create a .cursor/mcp.json file in your project directory. This allows you to define MCP servers that are only available within that specific project.
  • For tools that you want to use across all projects , create a ~/.cursor/mcp.json file in your home directory. This makes MCP servers available in all your Cursor workspaces.

Example mcp.json:

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

Option 2: Use a shell script

  1. Save a file called ragie-mcp.sh on your system:
#!/usr/bin/env bash

export RAGIE_API_KEY="your_api_key"

npx -y @ragieai/mcp-server --partition optional_partition_id
  1. Give the file execute permissions: chmod +x ragie-mcp.sh

  2. Add the MCP server script by going to Settings -> Cursor Settings -> MCP Servers in the Cursor UI.

Replace your_api_key with your actual Ragie API key and optionally set the partition ID if needed.

Claude Desktop Configuration

To use this MCP server with Claude desktop:

  1. Create the MCP config file claude_desktop_config.json:
  • For MacOS: Use ~/Library/Application Support/Claude/claude_desktop_config.json
  • For Windows: Use %APPDATA%/Claude/claude_desktop_config.json

Example claude_desktop_config.json:

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

Replace your_api_key with your actual Ragie API key and optionally set the partition ID if needed.

  1. Restart Claude desktop for the changes to take effect.

The Ragie retrieval tool will now be available in your Claude desktop conversations.

Features

Retrieve Tool

The server provides a retrieve tool that can be used to search the knowledge base. It accepts the following parameters:

  • query (string): The search query to find relevant information
  • topK (number, optional, default: 8): The maximum number of results to return
  • rerank (boolean, optional, default: true): Whether to try and find only the most relevant information
  • recencyBias (boolean, optional, default: false): Whether to favor results towards more recent information

The tool returns:

  • An array of content chunks containing matching text from the knowledge base

Development

This project is written in TypeScript and uses the following main dependencies:

  • @modelcontextprotocol/sdk: For implementing the MCP server
  • ragie: For interacting with the Ragie API
  • zod: For runtime type validation

Development setup

Running the server in dev mode:

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

Building the project:

npm run build

License

MIT License - See LICENSE.txt for details.

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