Vald Connector for AI agents.
6 live capabilities
Query and manage high-speed vector embeddings for RAG pipelines.
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Why people use Vald
Vald for High-Speed Vector Search Management
This Connector puts those controls directly into your AI client. You can just tell your agent to check the health of the cluster or update a specific record. It handles the communication with the Vald Gateway for you, so you can stay in your flow and treat your vector database like a conversational knowledge base.
What Vinkius changes
You get a direct line between your AI client and your distributed vector search engine.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,100+ Connectors
- Real-world use case 01
Debugging a broken RAG pipeline
A developer notices the AI is pulling irrelevant facts.
- Real-world use case 02
Rapidly testing new embeddings
An ML engineer wants to see how new model outputs look in the cluster.
- Real-world use case 03
Handling production errors
A DevOps engineer sees a spike in latency.
Complete set · 6capabilities
The complete Vald capability set.
These are the exact actions your AI can choose when you ask it to work with Vald.
01—03
3 capabilities in this set.
Part of 6 available through Vald.
- 01 Capability
Insert vector
Add a new vector and its unique ID into your Vald index. This is the primary way to ingest new data points.
- 02 Capability
Delete vector
Permanently remove a specific vector from the cluster. It is useful for cleaning up your search space.
- 03 Capability
Get engine info
Get the current health and operational status of your Vald engine. This helps you monitor cluster stability.
04—06
3 capabilities in this set.
Part of 6 available through Vald.
- 04 Capability
Get vector details
Pull the raw float array for a specific vector ID to check its contents. Use this to verify your embedding logic.
- 05 Capability
Update vector
Replace an existing vector's data with a new array using its ID. This keeps your records current without re-indexing.
- 06 Capability
Search vectors
Run a nearest neighbor similarity search using a query vector. This is what powers your semantic search results.
Set up in minutes
One URL. Then ask Vald to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Vald 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_LwabvjFRxR6SLDMyq0Lg8wB1fanBWcNw0aRuqiDM/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 Vald, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Vald for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_LwabvjFRxR6SLDMyq0Lg8wB1fanBWcNw0aRuqiDM/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 Vald URL.
- Step 03
Save and start
Save the connection and enable Vald in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"vald": {
"url": "https://edge.vinkius.com/vk_preview_LwabvjFRxR6SLDMyq0Lg8wB1fanBWcNw0aRuqiDM/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 Vald
Open Agent mode in chat and ask: "Using Vald, help me...". 6 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"vald": {
"url": "https://edge.vinkius.com/vk_preview_LwabvjFRxR6SLDMyq0Lg8wB1fanBWcNw0aRuqiDM/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 Vald
Ask Copilot: "Using Vald, help me...". 6 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"vald": {
"url": "https://edge.vinkius.com/vk_preview_LwabvjFRxR6SLDMyq0Lg8wB1fanBWcNw0aRuqiDM/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 Vald
Open Cascade and ask: "Using Vald, help me...". 6 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"vald": {
"url": "https://edge.vinkius.com/vk_preview_LwabvjFRxR6SLDMyq0Lg8wB1fanBWcNw0aRuqiDM/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 Vald
Ask Cline: "Using Vald, help me...". 6 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add vald --transport http "https://edge.vinkius.com/vk_preview_LwabvjFRxR6SLDMyq0Lg8wB1fanBWcNw0aRuqiDM/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 Vald
Ask Claude: "Using Vald, show me...". 6 tools are ready
Where the request belongs
Work Vald can move forward.
This is for the engineers who are tired of manual vector management. It's for the people building production-grade RAG systems who need to interact with their embeddings without constant context switching.
Machine Learning Engineer
Testing and visualizing embedding changes against a live cluster to ensure model quality.
Data Scientist
Validating search recall results by running top-k queries directly from the IDE.
DevOps Engineer
Checking engine health and node status during production anomalies to maintain uptime.
Backend Developer
Purging corrupted vectors or updating legacy records without needing to use a database terminal.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse ConnectorsMilvus (Open-Source Vector Database)
Manage vector storage via Milvus. perform ANN searches, query scalar entities, and audit collections.
Vertex AI Vector Search
Bring Google's massive vector matching power to your AI agent. Search billions of semantic embeddings and administer Vertex Index endpoints directly in chat.
Elasticsearch Vector
Empower vector search via Elasticsearch. perform dense vector kNN searches, handle index mappings, and index embedding documents directly from any AI agent.
Redis Vector
Equip your AI to autonomously manage embeddings, run KNN similarity searches, and administrate vector indexes natively inside your Redis stack.
OpenSearch Vector
Run k-NN vector searches on OpenSearch. create indexes, upsert embeddings, query similar documents, and manage your vector store from any AI agent.
Weaviate
Search and manage vector data on Weaviate. the AI-native database for building production-grade AI applications.
Bring your own AI
Change the model, client or framework. Keep Vald 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 Vald.
The practical details behind the request, access and result.
How does the Vald MCP help with RAG pipelines?
It gives your AI agent a direct way to interact with your vector database. You can search for relevant context, insert new data points, and manage your embeddings without writing any extra code.
Can I use Vald MCP to check if my vector cluster is online?
Yes. You can ask your agent to check the health of your Vald cluster at any time. It will pull the latest status from your Gateway so you know the engine is running smoothly.
Can I update specific vectors using the Vald MCP?
You can. If you have a specific ID, you can tell your agent to update that vector with a new array of floats. It handles the update process across your distributed cluster automatically.
Is it possible to delete vectors with the Vald MCP?
Yes, you can permanently remove specific vectors from your index. This is useful for cleaning up corrupted data or removing old records to keep your search results accurate.
How does the Vald MCP handle high-dimensional embeddings?
It's designed specifically for that. It lets your agent perform nearest neighbor searches across millions of high-dimensional points, making it ideal for complex similarity tasks.
Can I see the raw data of a vector using Vald MCP?
You can request the raw vector details for any specific ID. Your agent will pull the float array from the Vald index so you can verify the exact values stored in your database.
Can my AI agent do a semantic search across my vector database?
Yes! Provided you supply the embedded query vector, your agent can issue a vector search command to the Vald Engine. It will rapidly scan millions of indexes natively using its ANN algorithms and return the top-K closest neighbors associated with your data.
How do I ensure my Vald cluster is healthy right from my CLI?
Skip complex diagnostics loops. Instruct your agent to get Vald internal engine info. It will interface directly via gRPC/REST and pull down cluster metrics including operational status, agent versions, and basic diagnostic health. This is vital for MLOps managing production RAG pipelines needing constant reassurance.
Can I permanently purge a corrupted vector embedding?
When a document becomes stale in your knowledge base, you must remove its embedding. Ask the AI agent: permanently delete vector ID 'doc-xyz'. Using the removeVector capability, it targets your cluster and ensures the outdated semantic representation is fully expunged without risking other node data.
One connection away
Give your agent a direct line to Vald.
Connect Vald once. Keep it beside 6,100+ managed Connectors when the next task needs more.
Explore every Connector No credit card required · Free tier available