Azure AI Search Connector for AI agents.
6 live capabilities
Query enterprise knowledge bases with high-precision vector and full-text search.
Waiting for input…
Why people use Azure AI Search
Azure AI Search for Enterprise RAG Accuracy
With this Connector, your agent does the heavy lifting. Ask 'Is the blob storage syncing?' and it checks the indexers for you. You get a status report in your chat instead of a 20-minute audit.
What Vinkius changes
It turns your static Azure data into a searchable knowledge base for your AI.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Finding specific safety protocols
A user asks for a chemical spill procedure.
- Real-world use case 02
Checking sync health
An admin asks if the latest sales data is live.
- Real-world use case 03
Verifying index schemas
A developer needs to know the field types in the HR index.
Complete set · 6capabilities
The complete Azure AI Search capability set.
These are the exact actions your AI can choose when you ask it to work with Azure AI Search.
01—03
3 capabilities in this set.
Part of 6 available through Azure AI Search.
- 01 Capability
List indexes
See all your search indexes in one list. This helps you quickly identify which data sets are available for the agent to query.
- 02 Capability
Get index
Get the full configuration and schema for a specific index. You can use this to verify how your fields are mapped and which analyzers are active.
- 03 Capability
List indexers
View all your scheduled indexer tasks. It's the easiest way to see if your data is actually being pulled into the search service.
04—06
3 capabilities in this set.
Part of 6 available through Azure AI Search.
- 04 Capability
List datasources
See the data sources mapped to your search service. Use this to confirm that your agent knows where the source data lives, like a specific blob container.
- 05 Capability
Vector search
Perform high-relevance similarity searches across your embedding spaces. This is the primary way your agent finds context based on meaning rather than just keywords.
- 06 Capability
Search documents
Run a full-text query against your indices. This is perfect for when you need to find exact matches for specific terms, like a SKU or a specific date.
Set up in minutes
One URL. Then ask Azure AI Search to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Azure AI Search 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_UGnljcexY2MC0gUzqrUAvXNWXI0LuEzLwo7dKO1k/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 Azure AI Search, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Azure AI Search for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_UGnljcexY2MC0gUzqrUAvXNWXI0LuEzLwo7dKO1k/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 Azure AI Search URL.
- Step 03
Save and start
Save the connection and enable Azure AI Search in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"azure-ai-search": {
"url": "https://edge.vinkius.com/vk_preview_UGnljcexY2MC0gUzqrUAvXNWXI0LuEzLwo7dKO1k/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 Azure AI Search
Open Agent mode in chat and ask: "Using Azure AI Search, help me...". 6 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"azure-ai-search": {
"url": "https://edge.vinkius.com/vk_preview_UGnljcexY2MC0gUzqrUAvXNWXI0LuEzLwo7dKO1k/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 Azure AI Search
Ask Copilot: "Using Azure AI Search, help me...". 6 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"azure-ai-search": {
"url": "https://edge.vinkius.com/vk_preview_UGnljcexY2MC0gUzqrUAvXNWXI0LuEzLwo7dKO1k/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 Azure AI Search
Open Cascade and ask: "Using Azure AI Search, help me...". 6 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"azure-ai-search": {
"url": "https://edge.vinkius.com/vk_preview_UGnljcexY2MC0gUzqrUAvXNWXI0LuEzLwo7dKO1k/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 Azure AI Search
Ask Cline: "Using Azure AI Search, help me...". 6 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add azure-ai-search --transport http "https://edge.vinkius.com/vk_preview_UGnljcexY2MC0gUzqrUAvXNWXI0LuEzLwo7dKO1k/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 Azure AI Search
Ask Claude: "Using Azure AI Search, show me...". 6 tools are ready
Where the request belongs
Work Azure AI Search can move forward.
This is for the RAG engineer who needs to debug vector retrieval accuracy without leaving their IDE, or the cloud architect who needs to verify that data syncs aren't breaking at 3am.
RAG Engineer
Testing new embedding schemas and debugging vector retrieval accuracy without opening the Azure Portal.
Cloud Architect
Verifying the health of data sources and synchronized indexers moving unstructured data in real-time.
Data Scientist
Extracting precise contextual passages across massive Azure-backed corporate databases for analysis.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse ConnectorsAzure Cognitive Search
Empower your AI with enterprise retrieval. run full-text search, semantic queries, and inspect cognitive skillsets on your Azure indexes.
LlamaIndex (AI Data Framework & RAG)
Query and manage RAG pipelines via LlamaIndex. execute natural language searches, audit indexed files, and monitor data pipelines.
Amazon Bedrock KB
Connect your AI agent to AWS Bedrock Knowledge Bases. execute semantic searches, managed RAG, and sync vector datasources natively.
R2R
Equip your AI with direct access to your R2R engine. execute vector searches, run precise RAG queries, and manage your documents.
Haystack (deepset Cloud)
Build and manage AI-powered search and RAG pipelines via deepset Cloud. search documents, run pipelines, and manage workspaces.
Cognita (RAG Framework)
Manage modular RAG via Cognita. list collections, ingest data sources, and perform AI-driven Q&A directly from any AI agent.
Bring your own AI
Change the model, client or framework. Keep Azure AI Search 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 Azure AI Search.
The practical details behind the request, access and result.
Can the Azure AI Search MCP handle my company's private data?
Yes. It connects to your existing Azure environment, meaning your data stays within your secure cloud infrastructure while your agent queries it.
How does this help with RAG?
It provides the retrieval piece of Retrieval-Augmented Generation. Your agent uses it to pull relevant context from your massive datasets before generating an answer.
Can I check if my data is actually syncing?
Yes. You can ask your agent to check the status of your indexers to see if data is flowing correctly from your sources like Blob Storage or SQL.
Does this work for both vector and text search?
It supports both. You can use vector search for semantic meaning or full-text search for exact keyword matches like specific IDs or dates.
What happens if my indexer fails?
Your agent can detect the failure by checking the indexer status. It can tell you exactly which task is failing and what the error message is.
Is this the best way to connect Azure Search to Claude or Cursor?
It is the native way to do it. It allows those clients to interact with your Azure indexes directly without needing custom middleware or manual data exports.
Can my AI use this to query documents using vector embeddings directly?
Yes. If your agent is equipped with an embedding capability (like an OpenAI Ada dimension generator), it can compute the embedding float locally and transmit the precise K-Nearest Neighbors request into your Azure Index via the vector_search capability to return perfectly isolated contextual passages.
How can I verify if my Azure Search Indexer completed successfully?
You can ask your agent to list all indexers. It retrieves the scheduled background configurations defining how your Azure SQL or Blob stores migrate into Search form, allowing you to instantly assess if the pipeline is active or encountering extraction errors.
Can I audit the core configuration components of a specific index?
Absolutely. By providing the exact Index name, your AI fetches the exhaustive schema architecture: semantic mapping references, exact lexical BM25 fallback values, field weights, language analyzers, and HNSW graphs mapping vector space limits.
One connection away
Give your agent a direct line to Azure AI Search.
Connect Azure AI Search once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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