How to Use the Vertex AI Vector Search MCP in LangChain

Q: Can I track index status with Vertex AI Vector Search and LangChain?

Absolutely. You use getindexdetails to pull metadata for any specific index. This allows your agent to confirm the configuration before running a search, keeping your entire process reliable.

Q: What happens if I need to find all potential vector indexes using Vertex AI Vector Search and LangChain?

You call listvectorindexes to get a full catalog of every index in the project. This gives your agent options, letting it decide which dataset is appropriate for the current task.

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Connect Vertex AI Vector Search MCP to LangChain

Create your Vinkius account to connect Vertex AI Vector Search to LangChain 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 Vector Search with LangChain

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Managing Index Endpoints

You can list every index endpoint using `list_index_endpoints`. This lets your agent check all available connection points for vector search. The tool also provides `list_deployed_indexes`, helping the chain verify which indexes are ready to go.

Executing Vector Similarity Searches

When the user needs specific context, the agent calls `search_nearest_neighbors`. You just provide an endpoint ID, a deployed index ID, and the query vector. This returns the most relevant semantic matches for your chain to process.

Monitoring Vector Operations

The system tracks long-running jobs using `list_vector_operations`. If an index update takes time, your agent checks this list instead of waiting indefinitely. This ensures the entire reasoning pipeline doesn't hang up on background processes.

Setup guide

Set up Vertex AI Vector Search MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Vertex AI Vector Search tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "vertex-ai-vector-search-mcp": {
        "transport": "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,
    )
    result = await agent.ainvoke({
        "messages": "List recent Vertex AI Vector Search transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Vertex AI Vector Search MCP in LangChain

The MCP Server exposes vector tools directly to your agent. Your multi-step chain decides when to call `search_nearest_neighbors` based on intermediate results, making the search part of a larger process.

Absolutely. You use `get_index_details` to pull metadata for any specific index. This allows your agent to confirm the configuration before running a search, keeping your entire process reliable.

You call `list_vector_indexes` to get a full catalog of every index in the project. This gives your agent options, letting it decide which dataset is appropriate for the current task.

Yes. The server provides multiple tools that manage index endpoints (`list_index_endpoints`) and search capabilities. This gives your agent the necessary control points to perform robust, multi-step lookups.

This server handles vector index metadata and raw query vectors. It's focused on structured API configuration rather than user messaging or personal identifiers.

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