How to Use the Azure Synapse Analytics MCP in LlamaIndex

Q: How does LlamaIndex index data from Azure Synapse Analytics?

LlamaIndex calls tools like listpipelines through the MCP adapter and loads the JSON responses as Document objects. These documents are then vectorized and stored in your index, making your Synapse pipeline configurations searchable.

Q: Can I search my Azure Synapse Analytics notebooks using LlamaIndex?

Yes. Your LlamaIndex agent can run listnotebooks to pull your Spark notebook list, index the metadata, and let you perform semantic searches to locate specific data processing scripts.

Index your Azure Synapse Analytics metadata into LlamaIndex to query your data pipelines and compute pools with zero hallucinations.

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Connect Azure Synapse Analytics MCP to LlamaIndex

Create your Vinkius account to connect Azure Synapse Analytics to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Azure Synapse Analytics with LlamaIndex

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Ground LlamaIndex RAG in Live Synapse Metadata

The `list_notebooks` tool lets your LlamaIndex agent pull your active Synapse Spark notebooks directly into its local document index. By indexing these notebook names and paths, your agent can answer natural language queries about where specific ETL logic lives without guessing. This approach turns raw API outputs into searchable document chunks. When a user asks about Spark configurations, LlamaIndex queries this fresh index rather than relying on stale training data, ensuring your answers match your actual Synapse workspace.

Index Pipelines and Datasets with LlamaIndex MCP Server

The `list_pipelines` tool feeds your data integration pipelines directly into LlamaIndex's vector store for semantic search. Your agent can cross-reference these pipelines with live datasets retrieved via `list_datasets` to map out your entire data architecture. By wrapping this MCP Server in a LlamaIndex McpToolSpec, you allow your FunctionAgent to decide when to query the live Synapse API and when to search through its indexed history of your data pipelines.

Query Synapse Compute States Semantically

The `list_sql_pools` tool exposes your dedicated and serverless SQL pools to your LlamaIndex agent for real-time resource analysis. The agent can combine this with `list_spark_pools` to build a unified index of your active analytics compute. Instead of parsing JSON responses manually, your LlamaIndex agent queries this index using semantic search to find which Spark or SQL pools are currently provisioned and ready for workloads.

Setup guide

Set up Azure Synapse Analytics MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Azure Synapse Analytics MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Azure Synapse Analytics tools.",
)
response = await agent.run("List recent Azure Synapse Analytics data")

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Common questions about Azure Synapse Analytics MCP in LlamaIndex

LlamaIndex calls tools like `list_pipelines` through the MCP adapter and loads the JSON responses as Document objects. These documents are then vectorized and stored in your index, making your Synapse pipeline configurations searchable.

Yes. Your LlamaIndex agent can run `list_notebooks` to pull your Spark notebook list, index the metadata, and let you perform semantic searches to locate specific data processing scripts.

Yes, your LlamaIndex FunctionAgent can bypass the index and call tools like `get_pipeline` directly to fetch real-time pipeline definitions when you need the exact, current state of your Synapse workspace.

You install the llama-index-tools-mcp package, initialize the client with your Vinkius endpoint, and convert the tools using the MCP tool spec. From there, you pass them to your agent or query engine.

Your Synapse schema data—such as SQL pool configurations, Spark notebook lists, and pipeline definitions—is processed inside an isolated, ephemeral V8 container. Vinkius never caches your metadata, keeping your infrastructure details entirely private.

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