Vertex AI 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 Vertex AI Search as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
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 Vertex AI Search. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in Vertex AI 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 Vertex AI Search MCP Server
Connect your Vertex AI Search account to any AI agent and harness the power of Google's semantic search technology on your own enterprise data through natural conversation.
LlamaIndex agents combine Vertex AI 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
- Semantic Search — Perform high-quality semantic searches across documents with AI-powered relevance and accuracy
- Grounded Answers — Get direct, natural language answers grounded in your private document collection for reliable Q&A
- Data Stores — List and browse your enterprise data stores and search engines to manage your searchable datasets
- Document Discovery — Browse and list indexed documents within your data store branches directly from your agent
- Personalized Recommendations — Retrieve intelligent recommendations based on user interaction events and patterns
- Search Engines — View and manage high-level search applications configured for specific business use cases
The Vertex AI 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 Vertex AI Search to LlamaIndex via MCP
Follow these steps to integrate the Vertex AI 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 Vertex AI Search
Why Use LlamaIndex with the Vertex AI Search MCP Server
LlamaIndex provides unique advantages when paired with Vertex AI Search through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Vertex AI Search tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Vertex AI Search tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Vertex AI Search, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Vertex AI Search tools were called, what data was returned, and how it influenced the final answer
Vertex AI Search + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Vertex AI Search MCP Server delivers measurable value.
Hybrid search: combine Vertex AI Search real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Vertex AI 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 Vertex AI Search for fresh data
Analytical workflows: chain Vertex AI Search queries with LlamaIndex's data connectors to build multi-source analytical reports
Vertex AI Search MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect Vertex AI Search to LlamaIndex via MCP:
get_datastore_details
Retrieves configuration and metadata for a specific data store
get_grounded_answer
Returns a natural language response based on your private data. Retrieves an AI-generated answer grounded in the documents of a data store
get_recommendations
Provide a data store ID and user event data as a JSON object. Retrieves personalized recommendations based on user events
list_data_stores
Lists all data stores in the Vertex AI Search collection
list_datastore_documents
Provide data store and branch IDs. Lists all indexed documents within a specific data store branch
list_search_engines
Lists all search engines configured in the collection
search_documents
Provide a data store ID and the query text. Performs a search query across documents in a specific data store
Example Prompts for Vertex AI Search in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Vertex AI Search immediately.
"List all my available data stores in Vertex AI Search."
"Based on our documentation, what is our remote work policy?"
"Search the product catalog for 'blue wireless headphones'."
Troubleshooting Vertex AI Search MCP Server with LlamaIndex
Common issues when connecting Vertex AI Search to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpVertex AI Search + LlamaIndex FAQ
Common questions about integrating Vertex AI 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 Vertex AI 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.
AI-first code editor with integrated LLM-powered coding assistance.
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 Vertex AI Search to LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.