Knowledge Raven

Make your knowledge agent-ready.

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What it can do

  • List Knowledge Bases: Lists all available knowledge bases with document counts.
  • Search Knowledge Base: Targeted search for specific facts, definitions, or narrow questions. Returns up to 10 best matches with context. Each result includes a source_link for deep-linking to the cite
  • Broad Search: Keyword-heavy search for overview, comparison, and exploratory questions ('What tools do we use?', 'List all...'). Returns up to 30 results with broader matching. Each result includes a

What data it sees

Do you need an account

No: the server works without sign-in

Make your knowledge agent-ready. Connect docs from Confluence, Notion, GitHub, Dropbox, or Google Drive — any AI agent searches them via one MCP endpoint. 3 retrieval modes: vector search, broad search, and full document access.

How to connect

How to connect Knowledge Raven to Claude

The server address to paste into Claude settings:

https://rav3n--knowledge-raven.run.tools

  1. Open Claude (claude.ai or the desktop app).
  2. Go to Settings → Connectors.
  3. Click Add custom connector.
  4. Paste the server address copied below into Remote MCP server URL and click Add.
  5. In a chat, click + → Connectors and switch the new connector on.

Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.

Authorization

After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.

How to connect Knowledge Raven to ChatGPT

The server address to paste into ChatGPT settings:

https://rav3n--knowledge-raven.run.tools

  1. Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
  2. Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
  3. Open Settings → Connectors and click Create.
  4. Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
  5. Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
  6. In a new chat open + → Apps/Connectors and enable the connector.

OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.

Authorization

On first use ChatGPT opens the service's sign-in window. Sign in and approve access.

How to connect Knowledge Raven to Cursor

The server address to paste into Cursor settings:

https://rav3n--knowledge-raven.run.tools

Fastest: click Open in Cursor below and confirm the prompt.

Manually:

  1. In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
  2. Paste the JSON copied below into ~/.cursor/mcp.json (per project: .cursor/mcp.json in the repo root).
  3. Save the file. The server appears in the MCP list; authorize it there if asked.

Authorization

If the service needs sign-in, an authorize button appears next to the server in the MCP list.

Server tool list (6)

Raw names from tools/list. Only developers need these.

list_knowledge_basesLists all available knowledge bases with document counts.
search_knowledge_baseTargeted search for specific facts, definitions, or narrow questions. Returns up to 10 best matches with context. Each result includes a source_link for deep-linking to the cited passage. For overview or aggregation questions, use broad_search instead.
broad_searchKeyword-heavy search for overview, comparison, and exploratory questions ('What tools do we use?', 'List all...'). Returns up to 30 results with broader matching. Each result includes a source_link for deep-linking to the cited passage. For specific facts, use search_knowledge_base.
list_documentsLists all documents in a knowledge base with titles, types, AI-generated summaries, and source_link for each document. Use this to understand what content exists before searching, or when the user asks about available documents.
fetch_documentRetrieves document content flexibly. Modes: 'preview' (first 3 sections + summary), 'full' (entire document), 'chunks' (specific sections by index range, e.g. chunk_range=[5,10]). Returns a chunks array with per-chunk source_link for deep-linking to specific passages, plus a document-level source_link. Start with preview to check relevance before fetching full content.
get_document_metadataRetrieves metadata for a document (title, type, size, page count, etc.) including a source_link to view the document on the platform.