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Connect Vectara to ChatGPT, Claude and Gemini

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Give your AI agent direct access to your private data.

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

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

A futuristic MCP client docking bay
V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 7 capabilities

We ship only the actions that matter.

Capability set01 / 02

01-04

4 capabilities in this set.

Part of 7 available through Vectara.

  1. 01

    List corpora

    This capability returns a list of all searchable datasets currently in your Vectara account.

  2. 02

    List corpus documents

    Use this to see every indexed document contained within a specific corpus.

  3. 03

    Execute rag chat

    This capability runs a RAG-powered chat session that provides summarized answers with citations based on your provided corpus keys.

  4. 04

    Get corpus details

    This capability retrieves the specific metadata and configuration settings for a chosen corpus.

Capability set02 / 02

05-07

3 capabilities in this set.

Part of 7 available through Vectara.

  1. 05

    Delete corpus document

    This capability permanently removes a specific document from a corpus; this action cannot be undone.

  2. 06

    List chat sessions

    This capability pulls a list of your previous RAG chat sessions.

  3. 07

    Perform semantic search

    This capability executes a semantic search across one or more corpora using your query text.

Real work

What teams actually run

01

RAG Debugging

Test how your agent interprets specific documents by running queries against your corpora.

02

Internal Knowledge Retrieval

Ask questions against company wikis or technical manuals to get instant, cited answers.

03

Dataset Maintenance

Clean up your search index by deleting old or incorrect documents through your AI client.

04

Automated Documentation Search

Locate specific technical details across massive document libraries using natural language.

Automate a task

Put Vectara to work on one thing.

Step-by-step automations your AI runs through this Connector: the workflow, the prompts to start with, and the exact capabilities behind each move.

One connector, every AI

Vectara works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

ClaudeClaudeChatGPTChatGPTGeminiGeminiPerplexityPerplexityGrokGrokMicrosoft CopilotMicrosoft CopilotCursorCursorVS CodeVS CodeWindsurfWindsurfJetBrainsJetBrainsClineClineLangChainLangChainVercel AI SDKVercel AI SDKLovableLovableZ.aiZ.aiRaycastRaycastQwen CodeQwen CodeKimi CodeKimi CodeLe ChatLe Chat

Building your own app? The connector is yours to use.

You don't need a client to put Vectara to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

868ms average. Fast in production.

Vectara is checked daily against the live service.

Daily averagePeak 1195ms
Oct 3Today
Fastest day
745ms
Slowest day
1195ms
14-day trend
Slowing+24%

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

Guided setup for Claude? Set up Vectara in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Vectara owners hand off.

This MCP is built for technical professionals who need to bridge the gap between large document sets and their AI workflows.

  • 01

    Software Engineers

    They use it to test RAG responses directly in chat instead of writing test scripts.

  • 02

    Data Engineers

    They use it to manually remove stale data from Vectara using text commands.

  • 03

    Product Leads

    They query internal product manuals without needing a custom frontend.

  • 04

    Technical Writers

    They find specific passages across thousands of documents using semantic queries.

FAQ

Questions Vectara owners ask.

  • 01

    How do I connect Vectara to my AI client?

    You subscribe to the MCP on Vinkius, enter your Vectara API Key and Customer ID, and then connect your client like Claude or Cursor.

  • 02

    Can I delete documents using this MCP?

    Yes, you can use the delete_corpus_document capability to permanently remove a document from a corpus.

  • 03

    Does this support RAG?

    Yes, the execute_rag_chat capability allows your agent to perform RAG-powered chats with citations.

  • 04

    What clients are compatible?

    This MCP works with any MCP-compatible client, including Claude, Cursor, Windsurf, and VS Code.

  • 05

    Can I search multiple corpora at once?

    Yes, the perform_semantic_search capability allows you to provide multiple comma-separated corpus keys for a single query.

  • 06

    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.

  • 07

    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.

  • 08

    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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