How to Use the Zenserp MCP in LangChain

Q: What data types are processed when using Zenserp with LangChain?

The server touches text (snippets, titles), image URLs (searchimages), and structured business listings from searchmaps. The resulting output is always machine-readable JSON.

Q: Does Zenserp support different geographical areas for LangChain?

The searchgoogle tool supports optional location parameters (e.g., 'New York, NY'). This means you can build chains that target hyper-local market research across various geographies.

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Connect Zenserp MCP to LangChain

Create your Vinkius account to connect Zenserp 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 Zenserp with LangChain

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ChatGPT

Claude

Perplexity

Execute multi-source search queries

The `search_bing` tool pulls live results from Microsoft Bing. This lets your ReAct agent gather diverse data points—like product prices via `search_shopping` or local reviews using `search_maps`—and use them in sequence. The agent decides the exact order of calls based on intermediate outputs. You build a chain where one tool's result feeds directly into another, making complex research actionable.

Gather multimedia search data

Need more than just text? Use `search_images` to pull visual results or call `search_videos` for direct YouTube content. This lets your agent cross-reference textual findings with media evidence. The full set of tools, including specialized endpoints like `search_youtube`, gives the agent a multimedia view of a query. It ensures that every part of the answer is grounded in real search data.

Monitor current news and trends

The `search_news` tool returns articles with titles, snippets, and timestamps from Google News. This allows your agent to track breaking developments or monitor competitor activity instantly. When combined with historical data pulled by other tools, the chain identifies emerging themes. You're not just searching; you're building a real-time intelligence feed.

Setup guide

Set up Zenserp 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 Zenserp 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({
    "zenserp-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 Zenserp transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Zenserp MCP in LangChain

The MCP Server provides structured tool outputs that LangChain agents can interpret. Instead of raw HTML, your client gets clean JSON results from tools like `search_google`, allowing the agent to make logical decisions about which search result is most relevant for the next step.

Absolutely. The stateless nature of the MCP Client means you can aggregate tools from multiple sources—like combining `search_bing` with another API's toolset—into a single, coherent agent workflow.

The server touches text (snippets, titles), image URLs (`search_images`), and structured business listings from `search_maps`. The resulting output is always machine-readable JSON.

Yes. Because it scrapes live SERPs, the data reflects current search engine results. You get raw, verifiable information that your agent can process without relying on internal knowledge.

The `search_google` tool supports optional location parameters (e.g., 'New York, NY'). This means you can build chains that target hyper-local market research across various geographies.

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