This project provides the Model Context Protocol (MCP) Server, a backend service designed to integrate various data sources and tools with MCP-compatible clients. It enables users to ingest data from diverse platforms like Slack, Google Drive, and GitHub, transforming it into a searchable knowledge base for RAG applications. The target audience includes developers and product managers seeking a unified, RAG-ready knowledge layer across their toolchain.

How It Works

The server acts as a bridge, exposing a set of MCP tools for querying, retrieval, extraction, and publishing. It handles data ingestion from numerous sources, including file uploads, web crawling, and direct API integrations. Key features include built-in web search and transcription for audio/video, eliminating the need for external tool integrations. The architecture supports RAG prompt engineering and LLM conversations, with capabilities for structured data extraction and content publishing via audio or image generation.

Quick Start & Requirements

• Install/Run: npx -y graphlit-mcp-server
• Prerequisites: Node.js (v18.x+), Graphlit Platform account with API settings (Environment ID, Organization ID, JWT Secret).
• Configuration: Set environment variables (GRAPHLIT_ENVIRONMENT_ID, GRAPHLIT_ORGANIZATION_ID, GRAPHLIT_JWT_SECRET) or configure via JSON in VS Code/other MCP clients.
• Docs: Graphlit API Documentation
• Community: Discord

Highlighted Details

• Supports ingestion from over 20 data sources including Slack, Google Drive, Notion, Jira, and GitHub.
• Includes built-in web crawling and web search capabilities.
• Handles document conversion (PDF, DOCX to Markdown) and audio/video transcription.
• Offers tools for RAG prompt engineering, content extraction, and publishing via ElevenLabs or OpenAI.

Maintenance & Community

Support is available via GitHub Issues for the MCP Server and the Graphlit Discord community for broader platform support.

Licensing & Compatibility

The repository does not explicitly state a license in the README. Compatibility with commercial or closed-source applications is not specified.

Limitations & Caveats

The README does not specify a license, which may impact commercial adoption. Running npx on Windows might require explicit invocation via cmd.exe.