MCP For Claude Marketplace
Azure AI Search

Azure AI Search MCP Connector for Claude

A+

Execute RAG queries against Azure AI Search natively — search vectors, full-text documents, and audit cloud indexes directly from your AI agent.

6 tools Official Updated Jun 28, 2026 Official Vinkius Partner
More Details Connect to Claude

Connect your Azure AI Search endpoints to any AI agent and bring the power of enterprise RAG (Retrieval-Augmented Generation) directly into your conversational workflows.

What you can do

  • Vector & Full-Text Search — Execute precise K-Nearest Neighbors (KNN) retrieval or perform deep lexical BM25 BM25 queries against millions of documents
  • Indexes & Schemas — List your search indexes and inspect structural schema definitions including analyzers, vector profiles, and semantic configurations
  • Data Sources — Extract REST maps detailing where your Azure indexers securely source unstructured data (CosmosDB, Blob Containers, Azure SQL)
  • Indexers — Audit and monitor your scheduled synchronization agents pulling continuous state transitions synchronously

How it works

  1. Subscribe to this server
  2. Enter your Azure Search Endpoint and Admin / Query Key
  3. Start querying your enterprise knowledge bases securely from Claude, Cursor, or any MCP-compatible environment

Who is this for?

  • AI & RAG Engineers — test new embedding schemas, debug vector retrieval accuracy, and inspect BM25 indexing without opening the Azure Portal
  • Cloud Architects — verify the health of Data Sources and synchronized Indexers moving unstructured data in real-time
  • Data Scientists — instantly extract precise contextual passages across massive Azure-backed corporate databases
raginformation-retrievalsemantic-searchfull-text-searchcloud-indexingbm25

6 tools expose this connector's capabilities to your AI agent.

list_indexes

List all Azure AI Search indexes

get_index

Get explicit details of a single Azure search index configuration

list_indexers

List explicit scheduled Azure indexer tasks

list_datasources

List Azure AI Search data sources explicitly mapped

vector_search

Highly targeted relevance extraction spanning dimensional maps. Perform Azure vector similarity searches via explicit embedding spaces

search_documents

Execute lexical Full-Text search queries against Azure Indexes

See how to talk to your AI agent using Azure AI Search.

Show me the configuration schema for our 'corporate-docs-v2' index.

I've pulled the schema for 'corporate-docs-v2'. It contains 8 fields. The 'content_vector' field is mapped using HNSW cosine similarity. Semantic ranking is enabled. No language analyzers are explicitly overriding the default en.lucene settings.

List the Azure Search indexers and tell me if any are failing.

You have 2 indexers configured linking Blob containers. 'blob-sync-daily' is healthy on its schedule. However, 'cosmos-realtime-idxr' is reporting a configuration error — the destination container credentials appear disconnected on the backend.

Run a full-text lexical search for 'Q3 Financial Audits' in the reports index.

Searching the 'reports-index' via BM25 matching. I found 3 highly relevant paragraphs. The primary document `q3-audit.pdf` highlights a successful reconciliation process without anomalies. Would you like me to read the secondary findings?

Yes. If your agent is equipped with an embedding tool (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` tool to return perfectly isolated contextual passages.

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