Vertex AI Search MCP Server for LangChain 7 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Vertex AI Search through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with MultiServerMCPClient({
"vertex-ai-search": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Vertex AI Search, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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.
LangChain's ecosystem of 500+ components combines seamlessly with Vertex AI Search through native MCP adapters. Connect 7 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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 LangChain 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 LangChain via MCP
Follow these steps to integrate the Vertex AI Search MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 7 tools from Vertex AI Search via MCP
Why Use LangChain with the Vertex AI Search MCP Server
LangChain provides unique advantages when paired with Vertex AI Search through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine Vertex AI Search MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Vertex AI Search queries for multi-turn workflows
Vertex AI Search + LangChain Use Cases
Practical scenarios where LangChain combined with the Vertex AI Search MCP Server delivers measurable value.
RAG with live data: combine Vertex AI Search tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Vertex AI Search, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Vertex AI Search tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Vertex AI Search tool call, measure latency, and optimize your agent's performance
Vertex AI Search MCP Tools for LangChain (7)
These 7 tools become available when you connect Vertex AI Search to LangChain 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 LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting Vertex AI Search to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersVertex AI Search + LangChain FAQ
Common questions about integrating Vertex AI Search MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
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 LangChain
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.