Azure Cognitive Search MCP Server for LlamaIndex 7 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Azure Cognitive Search as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Azure Cognitive Search. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in Azure Cognitive Search?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Azure Cognitive Search MCP Server
Connect your Azure Cognitive Search endpoints to any AI agent and bring the power of enterprise information retrieval directly into your conversational workflows.
LlamaIndex agents combine Azure Cognitive Search tool responses with indexed documents for comprehensive, grounded answers. Connect 7 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Deep Search & Point-Reads — Execute full-text lexical queries across indexes or extract an exact, specific document mapping using its explicit UUID key
- Vector Retrieval — Inject structural arrays into predefined embedding domains for accurate, multidimensional K-Nearest Neighbor mapping
- Indexers & Skillsets — Discover active background tasks routing Azure blobs or databases, and inspect active Cognitive Services orchestrating OCR and text enrichment
- Schema Definitions — Trace exact token analyzers and dimensional shapes securing your cloud's query behaviors natively
The Azure Cognitive Search MCP Server exposes 7 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Azure Cognitive Search to LlamaIndex via MCP
Follow these steps to integrate the Azure Cognitive Search MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 7 tools from Azure Cognitive Search
Why Use LlamaIndex with the Azure Cognitive Search MCP Server
LlamaIndex provides unique advantages when paired with Azure Cognitive Search through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Azure Cognitive Search tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Azure Cognitive Search tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Azure Cognitive Search, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Azure Cognitive Search tools were called, what data was returned, and how it influenced the final answer
Azure Cognitive Search + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Azure Cognitive Search MCP Server delivers measurable value.
Hybrid search: combine Azure Cognitive Search real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Azure Cognitive Search to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Azure Cognitive Search for fresh data
Analytical workflows: chain Azure Cognitive Search queries with LlamaIndex's data connectors to build multi-source analytical reports
Azure Cognitive Search MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect Azure Cognitive Search to LlamaIndex via MCP:
get_document
Retrieve an exact single document mapped explicitly by its UUID key
get_index
Get Azure Cognitive Search index details
list_indexers
List explicitly scheduled Azure Search indexers
list_indexes
List Azure Search indexes
list_skillsets
List Cognitive Services skillsets orchestrating text enrichments
search_documents
Execute lexical full-text queries against Azure cognitive indexes
vector_search
Perform structural KNN vector searches against Azure embedding profiles
Example Prompts for Azure Cognitive Search in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Azure Cognitive Search immediately.
"Use the Get Document tool to show me the full raw JSON of record 'abc-1234'."
"List active Indexers and tell me if the blob-syncher is functioning."
"List all active skillsets enhancing our search environment currently."
Troubleshooting Azure Cognitive Search MCP Server with LlamaIndex
Common issues when connecting Azure Cognitive Search to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpAzure Cognitive Search + LlamaIndex FAQ
Common questions about integrating Azure Cognitive Search MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Azure Cognitive Search with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
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GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Azure Cognitive Search to LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.