How to Use the Vertex AI Search MCP in LlamaIndex

Q: Can I use Vertex AI Search with LlamaIndex to check document availability?

Yes. You can call listdatastoredocuments by passing in the data store and branch IDs, which confirms exactly what indexed documents are available for querying.

Q: What types of data can LlamaIndex query using Vertex AI Search?

It handles both static documents (via searchdocuments) and dynamic user event data (via getrecommendations), building a comprehensive knowledge graph for your application.

Q: How do I check the overall data structure available to LlamaIndex?

You use listdatastores first. This gives you an overview of all indexed collections, ensuring that every data source is accounted for before building your index.

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Connect Vertex AI Search MCP to LlamaIndex

Create your Vinkius account to connect Vertex AI Search 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 Vertex AI Search with LlamaIndex

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Creating unified knowledge bases

The unique power of this MCP Server is that its tool output becomes part of a searchable index. You use `get_grounded_answer` and the result gets indexed, meaning you can query past sessions or configurations later. Your LlamaIndex application builds RAG apps by combining live API data with static documents into one unified, queryable knowledge base.

Personalized insights using MCP Server

To get tailored suggestions, you call `get_recommendations`, providing both the necessary data store ID and user event data. This generates personalized results that go beyond simple keyword matching. These recommendations are crucial for building applications where the next best action depends on a history of user interactions.

Managing document indexes

You can list all indexed documents within a specific data store branch using `list_datastore_documents`. This helps validate that your source material is correctly loaded into the index. If you need to know what's in the system generally, running `list_data_stores` gives you an inventory of every available data store.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Vertex AI Search. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Vertex AI Search MCP in LlamaIndex

LlamaIndex indexes the results from this MCP Server into vector stores. This means your knowledge-augmented application can query API data and documents together, eliminating hallucinations.

The server provides the raw search data and grounded answers. LlamaIndex takes this output and turns it into a persistent index, letting you query deep into structured API results over time.

Yes. You can call `list_datastore_documents` by passing in the data store and branch IDs, which confirms exactly what indexed documents are available for querying.

It handles both static documents (via `search_documents`) and dynamic user event data (via `get_recommendations`), building a comprehensive knowledge graph for your application.

You use `list_data_stores` first. This gives you an overview of all indexed collections, ensuring that every data source is accounted for before building your index.

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