Graphlit MCP Server Integration

What does “Graphlit” MCP Server do?

The Graphlit MCP (Model Context Protocol) Server serves as a bridge between MCP clients and the Graphlit platform, enabling seamless integration with a wide array of external data sources and services. Its primary purpose is to aggregate, index, and make searchable diverse content from platforms like Slack, Discord, websites, Google Drive, email, Jira, Linear, and GitHub, transforming them into a unified, RAG-ready (Retrieval-Augmented Generation) knowledge base. The server supports ingestion of documents, web pages, audio, and video—automatically extracting or transcribing content for efficient retrieval. With built-in tools for web crawling, search, and more, Graphlit MCP Server empowers AI assistants and developers to interact with and manage large knowledge repositories, enabling advanced workflows such as document search, automated extraction, and multi-source aggregation within popular development environments.

List of Prompts

No explicit prompt templates are listed in the available documentation or repository files.

List of Resources

No explicit resources are detailed in the available documentation or repository files.

List of Tools

• Query Contents: Search for and retrieve content from the ingested knowledge base.
• Query Collections: Query specific collections of data or documents.
• Query Feeds: Retrieve and search through various feeds integrated into Graphlit.
• Query Conversations: Access and search conversation records across platforms.
• Retrieve Relevant Sources: Find sources relevant to a query or context.
• Retrieve Similar Images: Locate images that are visually similar to a provided image.
• Visually Describe Image: Generate a textual description of an image.
• Prompt LLM Conversation: Initiate or continue an LLM-based conversation for RAG workflows.
• Extract Structured JSON from Text: Convert unstructured text data into structured JSON format.
• Publish as Audio (ElevenLabs Audio): Convert content into audio using ElevenLabs.
• Publish as Image (OpenAI Image Generation): Generate images from prompts using OpenAI.
• Files, Web Pages, Messages, Posts, Emails, Issues, Text, Memory (Short-Term): Ingest these content types into Graphlit.
• Web Crawling: Perform automated web crawling to ingest web data.
• Data Connectors: Integrations for ingestion with:
  • Microsoft Outlook email
  • Google Mail
  • Notion
  • Reddit
  • Linear
  • Jira
  • GitHub Issues
  • Google Drive
  • OneDrive
  • SharePoint
  • Dropbox
  • Box
  • GitHub
  • Slack
  • Microsoft Teams
  • Discord
  • Twitter/X
  • Podcasts (RSS)

Use Cases of this MCP Server

• Enterprise Knowledge Management: Aggregate internal documents, communications, and resources from various platforms into a unified, searchable knowledge base for easy retrieval and RAG workflows.
• Automated Content Ingestion & Search: Automatically ingest documents, web pages, emails, and more—making them instantly searchable and accessible to AI assistants or developers.
• Multi-Source Retrieval-Augmented Generation (RAG): Enable LLMs to draw on up-to-date, context-rich information from diverse data sources, boosting the accuracy and relevance of AI-generated outputs.
• Cross-Platform Data Integration: Seamlessly connect and synchronize data from tools like Slack, Jira, GitHub, and Google Drive, facilitating holistic project and product management.
• Content Publishing & Transformation: Convert ingested content into other formats (audio, images) or extract structured data for further processing or publishing.

How to set it up

Windsurf

1. Ensure Node.js is installed on your system.
2. Locate or create your Windsurf configuration file.
3. Add the Graphlit MCP Server entry to the mcpServers section:

   {
     "mcpServers": {
       "graphlit": {
         "command": "npx",
         "args": ["@graphlit/graphlit-mcp-server@latest"]
       }
     }
   }
   

4. Save the configuration file and restart Windsurf.
5. Verify that the Graphlit MCP Server is running and accessible.

Securing API Keys

Use environment variables for API keys:

{
  "mcpServers": {
    "graphlit": {
      "command": "npx",
      "args": ["@graphlit/graphlit-mcp-server@latest"],
      "env": {
        "GRAPHLIT_API_KEY": "your-api-key"
      },
      "inputs": {
        "projectId": "your-project-id"
      }
    }
  }
}

Claude

1. Install Node.js if not already present.
2. Open Claude’s configuration file.
3. Add the Graphlit MCP Server entry as shown:

   {
     "mcpServers": {
       "graphlit": {
         "command": "npx",
         "args": ["@graphlit/graphlit-mcp-server@latest"]
       }
     }
   }
   

4. Save and restart Claude.
5. Confirm the server is listed in your connected MCP servers.

Cursor

1. Make sure Node.js is installed.
2. Edit the Cursor configuration file.
3. Insert the following MCP server configuration:

   {
     "mcpServers": {
       "graphlit": {
         "command": "npx",
         "args": ["@graphlit/graphlit-mcp-server@latest"]
       }
     }
   }
   

4. Save changes and restart Cursor.
5. Check that Graphlit MCP appears in available tools.

Cline

1. Confirm Node.js is available on your system.
2. Access your Cline configuration file.
3. Add the Graphlit MCP Server as follows:

   {
     "mcpServers": {
       "graphlit": {
         "command": "npx",
         "args": ["@graphlit/graphlit-mcp-server@latest"]
       }
     }
   }
   

4. Save and restart Cline.
5. Validate the MCP Server integration.

Note: Always use environment variables to secure sensitive information such as API keys, as shown in the Windsurf example above.

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:

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

Overview

Support for Roots: Not explicitly mentioned in the documentation.

Our opinion

Graphlit MCP Server is robust in tool functionality and integration guides but lacks explicit documentation on prompt templates and MCP resources. The presence of a LICENSE, active development, and strong GitHub engagement make it a solid choice for knowledge management and RAG use cases, though the absence of resource and prompt documentation may limit out-of-the-box adaptability in some scenarios.

MCP Score