How to Use the Alation MCP in LlamaIndex

Q: Can LlamaIndex query Alation assets directly?

You index the output of searchcatalog into a vector store. Once indexed, LlamaIndex performs semantic search to identify the right data assets.

Q: Does LlamaIndex support Alation saved queries?

Yes. The listsavedqueries tool retrieves your SQL snippets, which LlamaIndex then indexes for your agents to reuse.

Index your data assets with LlamaIndex and Alation to ground your agents in verified metadata.

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Connect Alation MCP to LlamaIndex

Create your Vinkius account to connect Alation 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 Alation with LlamaIndex

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Index Alation metadata with LlamaIndex

The `list_tables` tool provides the raw material for your LlamaIndex knowledge base. You pull table lists and store them as searchable entries. This turns your catalog into a vector store. Your agents query this index to find exactly which tables contain the data they need.

Ground LlamaIndex answers in Alation

Use `list_data_sources` to populate your index with current source information. LlamaIndex uses these details to ensure your agents never hallucinate about database names. You build RAG systems that rely on the catalog as the source of truth. It updates your knowledge base whenever your data sources change.

Retrieve catalog fields for LlamaIndex

The `list_custom_fields` tool lets you fetch governance metadata for your index. LlamaIndex then uses these definitions to clarify data ownership and usage policies. Your agents access these details during query time. It adds a layer of compliance to your automated data discovery process.

Setup guide

Set up Alation 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 Alation 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 Alation tools.",
)
response = await agent.run("List recent Alation data")

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Common questions about Alation MCP in LlamaIndex

You index the output of `search_catalog` into a vector store. Once indexed, LlamaIndex performs semantic search to identify the right data assets.

You use the MCP tool spec to convert catalog responses into indexed documents. This bridges the gap between raw catalog data and your agent's knowledge base.

Yes. The `list_saved_queries` tool retrieves your SQL snippets, which LlamaIndex then indexes for your agents to reuse.

The server remains stateless, but your LlamaIndex vector store retains the data you pull. You refresh the index periodically to stay current.

Your catalog details are stored in your private vector database. The transport layer uses tokenized authentication to prevent unauthorized data access.

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