Marqo AI (Vector Search & Embeddings) Connector for AI agents.
6 live capabilities
Manage your semantic search and vector embeddings through natural conversation.
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Why people use Marqo AI (Vector Search & Embeddings)
Marqo AI Vector Search for Semantic Data Management
This Connector puts your Marqo instance right in your AI agent's hands. You can check stats, create new indices, or run tensor searches just by talking to your AI client. You get a unified way to manage your search architecture without the constant context switching.
What Vinkius changes
You get a direct line of communication between your AI agent and your Marqo vector database.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Updating a product catalog
A developer needs to add 50 new items to a search index.
- Real-world use case 02
Debugging search relevance
A search architect isn't happy with results.
- Real-world use case 03
Cleaning up stale data
An engineer needs to remove old user profiles.
Complete set · 6capabilities
The complete Marqo AI (Vector Search & Embeddings) capability set.
These are the exact actions your AI can choose when you ask it to work with Marqo AI (Vector Search & Embeddings).
01—03
3 capabilities in this set.
Part of 6 available through Marqo AI (Vector Search & Embeddings).
- 01 Capability
Get index stats
Pull the configuration and stats for a specific index. Use it to check your document counts and embedding model types.
- 02 Capability
Tensor search
Execute a natural language tensor search on your data. The agent handles the embedding extraction so you just get the results.
- 03 Capability
Add documents
Write new documents into your Marqo vector indices. This lets you update your searchable data in real-time.
04—06
3 capabilities in this set.
Part of 6 available through Marqo AI (Vector Search & Embeddings).
- 04 Capability
Delete documents
Remove specific documents from Marqo using their unique IDs. It keeps your search index clean and relevant.
- 05 Capability
Create index
Create a new vector index with specific bounds and settings. This helps you set up new search architectures on the fly.
- 06 Capability
List indexes
See all your Marqo vector indexes at once. This helps you identify which collections are available before you run any queries.
Set up in minutes
One URL. Then ask Marqo AI (Vector Search & Embeddings) to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Marqo AI (Vector Search & Embeddings) from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_yWwQAD75WfnpbXY72FMh8SBRNTeZWrryXTJG2Ifa/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Marqo AI (Vector Search & Embeddings), and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Marqo AI (Vector Search & Embeddings) for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_yWwQAD75WfnpbXY72FMh8SBRNTeZWrryXTJG2Ifa/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Marqo AI (Vector Search & Embeddings) URL.
- Step 03
Save and start
Save the connection and enable Marqo AI (Vector Search & Embeddings) in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"marqo-ai-vector-search-embeddings": {
"url": "https://edge.vinkius.com/vk_preview_yWwQAD75WfnpbXY72FMh8SBRNTeZWrryXTJG2Ifa/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Marqo AI (Vector Search & Embeddings)
Open Agent mode in chat and ask: "Using Marqo AI (Vector Search & Embeddings), help me...". 6 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"marqo-ai-vector-search-embeddings": {
"url": "https://edge.vinkius.com/vk_preview_yWwQAD75WfnpbXY72FMh8SBRNTeZWrryXTJG2Ifa/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Marqo AI (Vector Search & Embeddings)
Ask Copilot: "Using Marqo AI (Vector Search & Embeddings), help me...". 6 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"marqo-ai-vector-search-embeddings": {
"url": "https://edge.vinkius.com/vk_preview_yWwQAD75WfnpbXY72FMh8SBRNTeZWrryXTJG2Ifa/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Marqo AI (Vector Search & Embeddings)
Open Cascade and ask: "Using Marqo AI (Vector Search & Embeddings), help me...". 6 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"marqo-ai-vector-search-embeddings": {
"url": "https://edge.vinkius.com/vk_preview_yWwQAD75WfnpbXY72FMh8SBRNTeZWrryXTJG2Ifa/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Marqo AI (Vector Search & Embeddings)
Ask Cline: "Using Marqo AI (Vector Search & Embeddings), help me...". 6 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add marqo-ai-vector-search-embeddings --transport http "https://edge.vinkius.com/vk_preview_yWwQAD75WfnpbXY72FMh8SBRNTeZWrryXTJG2Ifa/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Marqo AI (Vector Search & Embeddings)
Ask Claude: "Using Marqo AI (Vector Search & Embeddings), show me...". 6 tools are ready
Where the request belongs
Work Marqo AI can move forward.
This is for the engineers and architects who are tired of manual data entry and constant context switching between their vector database and their code editor.
Search Architect
Verifies index configurations and tests semantic relevance through natural conversation.
ML Engineer
Monitors vector index stats and checks embedding results without leaving the workspace.
Software Developer
Integrates AI search into apps and manages document lifecycles across different environments.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
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Chroma (Vector DB)
Manage vector embeddings via Chroma. list collections, query embeddings, and audit document counts directly from any AI agent.
Weaviate
Search and manage vector data on Weaviate. the AI-native database for building production-grade AI applications.
Elastic Enterprise Search
Manage enterprise search via Elastic. search engines and documents, handle indexing, and monitor search analytics directly from any AI agent.
Qdrant
Empower your AI to interact directly with your Qdrant vector database. query clusters, perform similarity searches, and manage collections effortlessly.
Bring your own AI
Change the model, client or framework. Keep Marqo AI connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Marqo AI.
The practical details behind the request, access and result.
How does the Marqo AI MCP help with my search results?
It lets your AI agent perform tensor searches directly on your Marqo data. Instead of just matching words, it understands the meaning behind your queries to give you better results.
Can I use Marqo AI to add new data to my vector database?
Yes. You can tell your agent to add new JSON documents to your Marqo indices whenever you need to. It handles the indexing for you automatically.
Is the Marqo AI MCP good for managing large vector indices?
It's built for that. You can use it to list all your indices, check their stats, and monitor your document counts to keep your search infrastructure organized.
How do I delete old records from my Marqo instance?
You can ask your agent to remove specific documents by their unique IDs. This helps keep your search index clean and relevant.
Can I create new search collections with this Connector?
Yes, you can use it to create new vector indices with specific dimensions and model settings. It's a fast way to expand your search capabilities.
Does the Marqo AI MCP work with my existing Marqo account?
It does. You just need to provide your Marqo API URL and API Key to connect your existing instance to your AI agent.
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.
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.
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.
One connection away
Give your agent a direct line to Marqo AI.
Connect Marqo AI once. Keep it beside 5,900+ managed Connectors when the next task needs more.
Explore every Connector No credit card required · Free tier available