Vertex AI Vector Search MCP Server for LlamaIndex 6 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Vertex AI Vector 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 Vector Search. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in Vertex AI Vector 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 Vector Search MCP Server
Plug the sheer matching scale of Google Cloud's Vertex AI Vector Search directly into your intelligent IDE or conversational agent. Unleash low-latency nearest neighbor lookups across billion-scale embedding structures without navigating Cloud Console interfaces.
LlamaIndex agents combine Vertex AI Vector Search tool responses with indexed documents for comprehensive, grounded answers. Connect 6 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
- Massive Semantic Extraction — Prompt your agent to formulate query vectors and blast them at your specialized Cloud endpoints. It instantly retrieves identical geometric text boundaries (nearest neighbors) to ground LLM contexts powerfully.
- Index Operations — Gain total situational awareness over your massive datasets. Command the bot to list your provisioned Vector Indexes, verifying dimensionality, configuration updates, and current active states within seconds.
- Endpoint Monitoring — List active network endpoints scaling your specific RAG applications. Determine clearly which underlying deployed index iterations are currently receiving production traffic without digging through IAM screens.
- Operation Tracking — Spun up a multi-terabyte index build? Query the cloud queue using chat to review persistent long-running task timelines from your primary editor.
The Vertex AI Vector Search MCP Server exposes 6 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 Vector Search to LlamaIndex via MCP
Follow these steps to integrate the Vertex AI Vector 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 6 tools from Vertex AI Vector Search
Why Use LlamaIndex with the Vertex AI Vector Search MCP Server
LlamaIndex provides unique advantages when paired with Vertex AI Vector Search through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Vertex AI Vector Search tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Vertex AI Vector Search tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Vertex AI Vector Search, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Vertex AI Vector Search tools were called, what data was returned, and how it influenced the final answer
Vertex AI Vector Search + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Vertex AI Vector Search MCP Server delivers measurable value.
Hybrid search: combine Vertex AI Vector Search real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Vertex AI Vector 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 Vector Search for fresh data
Analytical workflows: chain Vertex AI Vector Search queries with LlamaIndex's data connectors to build multi-source analytical reports
Vertex AI Vector Search MCP Tools for LlamaIndex (6)
These 6 tools become available when you connect Vertex AI Vector Search to LlamaIndex via MCP:
get_index_details
Retrieves metadata and configuration for a specific vector index
list_deployed_indexes
Lists all indexes deployed to a specific endpoint
list_index_endpoints
Lists all index endpoints in the project
list_vector_indexes
Lists all vector indexes in the Google Cloud project
list_vector_operations
Lists long-running operations related to vector indexes
search_nearest_neighbors
Provide the endpoint ID, deployed index ID, and a query vector as a JSON array. Performs a nearest neighbor vector similarity search
Example Prompts for Vertex AI Vector Search in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Vertex AI Vector Search immediately.
"List all our active vector indexes on the current GCP project."
"Check for any long-running vector deployment operations currently uncompleted."
"Find the 3 nearest neighbors mapping to endpoint '39xl' array index ID 'dep_30' using vector [-0.2, 0.5, 0.0]."
Troubleshooting Vertex AI Vector Search MCP Server with LlamaIndex
Common issues when connecting Vertex AI Vector Search to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpVertex AI Vector Search + LlamaIndex FAQ
Common questions about integrating Vertex AI Vector 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 Vector 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 Vector Search to LlamaIndex
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.