How to Use the Vertex AI Vector Search MCP in LlamaIndex

Q: Can I check which indexes are deployed using Vertex AI Vector Search and LlamaIndex?

You call listdeployedindexes to see exactly what's running. This is crucial for building a reliable RAG application, ensuring the knowledge index points to live endpoints.

Q: What if I need to verify an index's structure using Vertex AI Vector Search and LlamaIndex?

Use getindexdetails to pull all configuration parameters for a specific vector index. This lets your agent confirm the schema before indexing results, preventing data mismatch issues.

Q: Does the MCP Server support querying multiple datasets using Vertex AI Vector Search and LlamaIndex?

Yes. By combining listvectorindexes with the search tool, your agent can build a unified index structure that pulls knowledge from various sources across the project.

Index Everything: Augment Knowledge with LlamaIndex and Vertex AI Vector Search.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Vertex AI Vector Search MCP to LlamaIndex

Create your Vinkius account to connect Vertex AI Vector 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.

GDPR Free for Subscribers

Setup Vertex AI Vector Search with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Creating Semantic Searches

When you run a query, `search_nearest_neighbors` finds the closest semantic matches using Google's embeddings. The output feeds directly into your knowledge base, allowing LlamaIndex to ground its answers in current API data.

Managing Index Metadata

Need to know what indexes are available? Use `list_vector_indexes` or check the project overview with `list_index_endpoints`. This ensures your RAG application always targets a valid, active data source.

Handling Index Lifecycle

If an index is undergoing changes, you track it using `list_vector_operations`. Your LlamaIndex workflow can wait for or check these operations before attempting to query the underlying MCP Server connection.

Setup guide

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

Get your connection token →

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 Vector 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Vertex AI Vector Search now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Vertex AI Vector Search MCP in LlamaIndex

The MCP tool output becomes part of your searchable index. Instead of relying on general knowledge, your agent can query past session data or specific API configurations directly from the vector store.

You call `list_deployed_indexes` to see exactly what's running. This is crucial for building a reliable RAG application, ensuring the knowledge index points to live endpoints.

Use `get_index_details` to pull all configuration parameters for a specific vector index. This lets your agent confirm the schema before indexing results, preventing data mismatch issues.

Yes. By combining `list_vector_indexes` with the search tool, your agent can build a unified index structure that pulls knowledge from various sources across the project.

This server handles vector index metadata and raw query vectors. The focus is on structured API configuration rather than user communications or personally identifiable information.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript