Graphlit MCP Server

The Graphlit MCP Server transforms the Graphlit platform into a fully‑featured, AI‑ready knowledge base that developers and product teams can query from any MCP client. By ingesting content from a wide range of sources—Slack, Discord, Jira, Linear, GitHub, Google Drive, email, and even web pages—Graphlit automatically extracts, normalizes, and stores data as searchable Markdown or transcribed text. This turns disparate communications and documentation into a single, coherent repository that an LLM can consult in real time.

What makes this server valuable is its seamless integration with existing AI workflows. Once a project is created on Graphlit, the MCP server exposes tools that let an assistant perform retrieval‑augmented generation (RAG), structured data extraction, and even media publishing—all through simple tool calls. Developers can ask an assistant to “search the latest GitHub issues for bugs in project X” or “retrieve a PDF from Google Drive and summarize it,” and the assistant will pull the relevant content directly from Graphlit’s indexed store. The server also supports advanced querying across collections, feeds, and conversations, enabling contextual answers that span multiple documents or chat histories.

Key capabilities include:

• Universal ingestion of files, web pages, messages, posts, emails, and issue trackers, with automatic Markdown conversion or transcription for audio/video.
• Built‑in web crawling and search, eliminating the need for external scraping tools.
• RAG tooling that lets clients prompt an LLM to generate responses grounded in the indexed content.
• Structured extraction of JSON from arbitrary text, useful for turning natural language into data objects.
• Publishing utilities such as audio generation via ElevenLabs and image creation through OpenAI’s API.
• Rich data connectors covering cloud storage, collaboration suites, and social platforms, all accessible through a single API surface.
• Operational tools for managing collections, feeds, and content lifecycle directly from the MCP client.

Real‑world scenarios span technical documentation support (e.g., auto‑answering queries about a codebase), product management (pulling insights from Jira tickets or Linear projects), and compliance (searching email archives for policy references). By exposing a unified search layer, Graphlit enables AI assistants to deliver precise, context‑aware answers without requiring developers to write custom integrations for each data source.

In practice, a developer can create a Graphlit project, configure the MCP server with their environment credentials, and start using any MCP‑compliant client—Cursor, Goose, Windsurf, or Cline—to query the knowledge base. The server’s extensive toolset and robust data connectors give teams a powerful, low‑overhead way to harness their existing information silos through conversational AI.