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ClaudeChatGPTPerplexityGeminiMicrosoft CopilotRaycastMeta AIGrokZ.aiQwenKimi
DeepSeekMistralCursorVS CodeWindsurfJetBrainsClineLovableVercel AI SDKLangChain

Use Vectara with your AI.

Connect your account once and let the AI you already use work with it, without building another integration. Empower your agent with Vectara's RAG capabilities. Search corpora natively, execute grounded chats, and manage indexed datasets easily.

Included with plan

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

Complete set · 7 capabilities

The complete Vectara capability set.

These are the exact actions your AI can choose when you ask it to work with Vectara.

Capability set01 / 02

01-04

4 capabilities in this set.

Part of 7 available through Vectara.

  1. 01

    List corpora

    Lists all corpora (searchable datasets) in the Vectara account

  2. 02

    List corpus documents

    Lists all indexed documents within a specific corpus

  3. 03

    Execute rag chat

    Provide corpus keys and the user query to get a summarized AI response with citations. Executes a RAG-powered chat completion

  4. 04

    Get corpus details

    Retrieves metadata and configuration for a specific corpus

Capability set02 / 02

05-07

3 capabilities in this set.

Part of 7 available through Vectara.

  1. 05

    Delete corpus document

    This action is irreversible. Permanently removes a document from a corpus

  2. 06

    List chat sessions

    Lists previous RAG chat sessions

  3. 07

    Perform semantic search

    Provide one or more comma-separated corpus keys and the query text. Executes a semantic search across one or more corpora

Observed, not estimated

875ms average. Fast in production.

Vectara is checked daily against the live service.

Daily averagePeak 1197ms
Aug 20Today
Fastest day
697ms
Slowest day
1197ms
14-day trend
Slowing+11%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 7 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Vectara, so you can see the experience inside your AI.

It does not authenticate your account with Vectara. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Vectara Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_P55Xdk6UU5XMN3cdVPORquufduaGCfpEWSemsNQg/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — Vectara capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "vectara-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_P55Xdk6UU5XMN3cdVPORquufduaGCfpEWSemsNQg/mcp"
    }
  }
}
  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Step-by-step instructions for each client are in the guide. How to connect

FAQ

Questions Vectara owners ask.

  • 01

    Can I query my internal documents directly using just conversational chat?

    Yes. If your data is indexed in a Vectara corpus, simply ask your agent: search the 'employee-handbook' corpus for remote work policies. The agent uses the queryTool to pass your question to Vectara's semantic engine, effortlessly bringing back precisely matching paragraph citations instantly.

  • 02

    How do I remove outdated context files destroying the accuracy of my RAG model?

    You don't need to rebuild APIs or use cURL. Tell your AI: delete document ID 'doc-992a' from my Sales corpus. It automatically formats the mutation and wipes the poisoned embedding from Vectara's nodes permanently, restoring high accuracy.

  • 03

    Will the RAG Chat capability provide accurate source citations?

    Yes. When you instruct the agent to run execute_rag_chat, Vectara processes the query against its internal LLM and index, returning a synthesized natural language answer appended solidly with exact document citations, proving the AI isn't hallucinating facts.

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