Ragie MCP Server

What does “Ragie” MCP Server do?

The Ragie MCP (Model Context Protocol) Server serves as an interface between AI assistants and Ragie’s knowledge base retrieval system. By implementing the MCP, this server enables AI models to query a Ragie knowledge base, facilitating the retrieval of relevant information to support advanced development workflows. The primary functionality offered is the ability to perform semantic search and fetch contextually pertinent data from structured knowledge bases. This integration empowers AI assistants with enhanced capabilities for knowledge retrieval, supporting tasks such as answering questions, providing references, and integrating external knowledge into AI-driven applications.

List of Prompts

No prompt templates are mentioned in the available documentation.

List of Resources

No explicit resources are documented in the available repository files or README.

List of Tools

• retrieve: Allows querying the Ragie knowledge base for relevant information. This is the main and only tool exposed by the Ragie MCP Server.

Use Cases of this MCP Server

• Knowledge Base Querying: Developers can use the server to perform semantic searches within a Ragie knowledge base, retrieving information relevant to their queries.
• AI Augmentation: Enables AI assistants and agents to supplement their responses with facts or context fetched from the knowledge base.
• Automated Research: Assists in automating information gathering for research, documentation, or analysis tasks by leveraging Ragie’s retrieval capabilities.
• Contextual Answer Generation: Enhances LLM-driven applications by providing them with up-to-date or domain-specific knowledge not inherently present in the model.

How to set it up

Windsurf

1. Ensure Node.js (>= 18) is installed.
2. Obtain your Ragie API key.
3. Edit or create the MCP configuration file in Windsurf.
4. Add the Ragie MCP server with the following JSON snippet:

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

5. Save changes and restart Windsurf. Verify the server is running.

Claude

1. Install Node.js (>= 18).
2. Acquire your Ragie API key.
3. Update the Claude MCP configuration.
4. Insert the Ragie MCP server configuration:

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

5. Restart the Claude client and ensure connectivity.

Cursor

1. Confirm Node.js (>= 18) is set up.
2. Obtain the Ragie API key.
3. Edit the Cursor configuration for MCP servers.
4. Add:

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

5. Save and restart Cursor.

Cline

1. Make sure Node.js (>= 18) is present.
2. Get your Ragie API key.
3. Open Cline’s MCP server config file.
4. Add:

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

5. Save the file and restart Cline.

Securing API Keys:
Always provide the RAGIE_API_KEY via environment variables, not in source code or configuration files directly.
Example:

{
  "env": {
    "RAGIE_API_KEY": "your_api_key"
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "ragie": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “ragie” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Our opinion

The Ragie MCP Server is highly focused and easy to set up, with clear documentation for tool integration and API key security. However, it currently offers only one tool, no explicit prompt or resource templates, and lacks details on advanced features like roots or sampling.

MCP Score

Rating:
Based on the above tables, we’d rate the Ragie MCP Server a 5/10. It is well-licensed, clearly documented, and simple, but limited in scope and extensibility due to the absence of prompts, resources, roots, or sampling. Suitable for basic KB retrieval, but not for complex workflows requiring richer protocol features.