Zenserp MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Zenserp through 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({
"zenserp": {
"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 Zenserp, 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 Zenserp MCP Server
Connect your Zenserp account to any AI agent and harness the power of real-time search intelligence through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with Zenserp through native MCP adapters. Connect 10 tools via 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
- Organic Search — Retrieve structured organic results from Google, Bing, Yandex, and DuckDuckGo including titles, URLs, and snippets
- Image Discovery — Find high-quality images and retrieve direct source or thumbnail URLs across the major search engines
- Local Intelligence — Search Google Maps for business listings, physical addresses, ratings, and reviews for any location
- News Monitoring — Retrieve breaking stories and current articles from Google News with precise timestamps and source metadata
- E-commerce Auditing — Compare product prices and availability by scraping Google Shopping results into structured JSON
- Video Search — Find indexed videos across various platforms through Google Video and YouTube search tools
- Geographic Precision — Execute searches with specific location parameters (e.g., 'New York, NY') to see localized results
The Zenserp MCP Server exposes 10 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 Zenserp to LangChain via MCP
Follow these steps to integrate the Zenserp 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 10 tools from Zenserp via MCP
Why Use LangChain with the Zenserp MCP Server
LangChain provides unique advantages when paired with Zenserp through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Zenserp 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 Zenserp queries for multi-turn workflows
Zenserp + LangChain Use Cases
Practical scenarios where LangChain combined with the Zenserp MCP Server delivers measurable value.
RAG with live data: combine Zenserp tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Zenserp, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Zenserp tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Zenserp tool call, measure latency, and optimize your agent's performance
Zenserp MCP Tools for LangChain (10)
These 10 tools become available when you connect Zenserp to LangChain via MCP:
search_bing
Retrieves organic search results from Microsoft Bing
search_duckduckgo
Retrieves organic search results from DuckDuckGo
search_google
Provide a query string and optional location (e.g. "New York,NY"). Retrieves organic search results from Google
search_images
Retrieves image search results from Google
search_maps
Retrieves local business listings and reviews from Google Maps
search_news
Returns articles with titles, snippets, and timestamps. Retrieves current news articles from Google News
search_shopping
Retrieves product prices and availability from Google Shopping
search_videos
Retrieves video search results from Google Video search
search_yandex
Retrieves search results from the Yandex search engine
search_youtube
Retrieves search results directly from the YouTube platform
Example Prompts for Zenserp in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Zenserp immediately.
"Search Google for 'best CRM software for small business' and show me the top 5 organic results."
"Find restaurants in 'Austin, TX' using Google Maps and show their ratings."
"What are the current news headlines for 'generative AI'?"
Troubleshooting Zenserp MCP Server with LangChain
Common issues when connecting Zenserp to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersZenserp + LangChain FAQ
Common questions about integrating Zenserp 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 Zenserp 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 Zenserp to LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.