Langflow-DOC-QA-SERVER MCP Server

What does “Langflow-DOC-QA-SERVER” MCP Server do?

Langflow-DOC-QA-SERVER is a Model Context Protocol (MCP) server designed for document question-and-answer (Q&A) tasks, powered by Langflow. It acts as a bridge between AI assistants and a Langflow backend, allowing users to query documents in a streamlined way. By leveraging MCP, this server exposes document Q&A capabilities as tools and resources that can be accessed by AI clients, thus enabling advanced development workflows. Developers can integrate document retrieval, question answering, and interaction with large language models (LLMs) into their applications, making it easier to enhance productivity in tasks like documentation search, support automation, and information extraction.

List of Prompts

No prompt templates are documented in the repository or README.

List of Resources

No specific resources are documented or listed in the repository or README.

List of Tools

No explicit tools are listed in a server.py or equivalent server file in the available documentation or file listing.

Use Cases of this MCP Server

• Document Search and Q&A
  Integrate natural language search over documents for instant answers, improving access to organizational knowledge.
• Automated Support Bots
  Use the server as a backend for bots that answer user questions based on uploaded or indexed documentation.
• Knowledge Management
  Enable teams to extract information from large collections of documents, enhancing productivity.
• Workflow Automation
  Automate repetitive research or information retrieval tasks by embedding document Q&A capabilities in workflows.

How to set it up

Windsurf

1. Ensure prerequisites are installed (e.g., Node.js, Langflow backend).
2. Open your Windsurf configuration file.
3. Add the Langflow-DOC-QA-SERVER MCP server using the following JSON snippet:

   {
     "mcpServers": {
       "langflow-doc-qa": {
         "command": "npx",
         "args": ["@GongRzhe/Langflow-DOC-QA-SERVER@latest"]
       }
     }
   }
   

4. Save the configuration and restart Windsurf.
5. Verify that the server is running and accessible.

Securing API Keys

Use environment variables to secure API keys:

{
  "mcpServers": {
    "langflow-doc-qa": {
      "command": "npx",
      "args": ["@GongRzhe/Langflow-DOC-QA-SERVER@latest"],
      "env": {
        "API_KEY": "${API_KEY}"
      },
      "inputs": {
        "api_key": "${API_KEY}"
      }
    }
  }
}

Claude

1. Install required dependencies.
2. Locate the Claude configuration file.
3. Add the MCP server configuration as shown above.
4. Restart Claude.
5. Confirm connectivity to the Langflow-DOC-QA-SERVER.

Cursor

1. Prepare the Langflow backend and install Node.js if needed.
2. Edit the Cursor configuration.
3. Insert the MCP server configuration JSON.
4. Save changes and restart Cursor.
5. Test the server integration.

Cline

1. Ensure all prerequisites are met.
2. Update the Cline configuration file.
3. Add the MCP server JSON configuration.
4. Restart Cline for changes to take effect.
5. Validate the integration.

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:

{
  "langflow-doc-qa": {
    "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 “langflow-doc-qa” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Our opinion

The Langflow-DOC-QA-SERVER MCP is a minimal, demonstration-focused server that clearly explains its purpose and setup but lacks documentation on prompt templates, resources, and tools. Its setup instructions are generic and based on standard MCP conventions. This limits its out-of-the-box utility but makes it a clear example for basic integration.

MCP Score

Rating: 4/10 — The project is well-scoped and open source, but lacks rich documentation and detail on its MCP-specific features, resources, and tools.