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Connect Marqo AI Vector Search Embeddings to your AI

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. Manage vector indices and run tensor queries through your AI agent.

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 · 6 capabilities

We ship only the actions that matter.

Capability set01 / 02

01-03

3 capabilities in this set.

Part of 6 available through Marqo AI.

  1. 01

    Get index stats

    Retrieves configuration details and performance metrics for a specific index. Use this to check document counts and embedding model settings.

  2. 02

    Create index

    Sets up a new, explicitly bounded vector index. You can define custom model settings and dimension constraints during creation.

  3. 03

    Delete documents

    Removes specific documents from your Marqo index. You target individual scalar identifiers to keep your search data clean.

Capability set02 / 02

04-06

3 capabilities in this set.

Part of 6 available through Marqo AI.

  1. 04

    Add documents

    Writes new JSON records into your existing Marqo indices. This makes fresh data searchable immediately after ingestion.

  2. 05

    List indexes

    Shows all available vector indices on your Marqo instance. Use this to verify your collection boundaries before running queries.

  3. 06

    Tensor search

    Runs natural language tensor searches against your indices. Marqo handles the embedding extraction automatically based on your query.

Real work

What teams actually run

01

Rapid Prototyping

Test how different natural language queries perform against your existing vector indices.

02

Data Maintenance

Clean up your search index by removing outdated documents using specific IDs.

03

Index Monitoring

Check the stats and schema mappings of your indices to ensure they meet your application requirements.

04

Dynamic Content Updates

Feed new data into your search engine by sending JSON records through your AI client.

Automate a task

Put Marqo AI 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

Marqo AI 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 Marqo AI 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

906ms average. Fast in production.

Marqo AI is checked daily against the live service.

Daily averagePeak 1206ms
Oct 3Today
Fastest day
737ms
Slowest day
1206ms
14-day trend
Slowing+13%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 6 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 Marqo AI, so you can see the experience inside your AI.

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

Marqo AI Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_yWwQAD75WfnpbXY72FMh8SBRNTeZWrryXTJG2Ifa/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 — Marqo AI capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "marqo-ai-vector-search-embeddings-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_yWwQAD75WfnpbXY72FMh8SBRNTeZWrryXTJG2Ifa/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 Marqo AI in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Marqo AI owners hand off.

This MCP is built for technical professionals who need to interact with vector databases through a conversational interface.

  • 01

    Search Architects

    They use the MCP to test semantic relevance and verify index configurations via natural language.

  • 02

    Machine Learning Engineers

    They monitor vector index stats and verify embedding results directly from their workspace.

  • 03

    Software Developers

    They manage document lifecycles and integrate search results into applications using their AI agent.

FAQ

Questions Marqo AI owners ask.

  • 01

    How do I connect my Marqo instance?

    You subscribe to the MCP on Vinkius and enter your Marqo API URL and API Key. Once connected, your AI client can immediately start using the capabilities.

  • 02

    Which AI clients can use this MCP?

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

  • 03

    Can I create new indices through my AI agent?

    Yes, you can use the create_index capability to set up new vector indices with custom model settings and dimension constraints.

  • 04

    Does the search require manual embedding?

    No, when you use the tensor_search capability, Marqo handles the embedding extraction automatically.

  • 05

    Can I see how many documents are in my index?

    Yes, you can use the get_index_stats capability to retrieve detailed configuration metrics, including document counts.

  • 06

    Does Marqo handle the vector embeddings for me through the agent?

    Yes. Marqo is an end-to-end engine. When you use the tensor_search capability, you provide natural language and Marqo handles the model inference and vector extraction under the hood, returning semantically relevant results immediately.

  • 07

    Can I add new data to a vector index through a conversation?

    Absolutely. Use the add_documents capability by providing a JSON array of your documents. Your agent will synchronize these records into the target index, and they will be searchable via semantic query instantly.

  • 08

    How do I check the stats of my vector index?

    The get_index_stats capability retrieves critical metrics for a specific index. Your agent will report the document count, memory usage, and configuration details, helping you monitor the health of your vector store.

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