Zenserp MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Zenserp as an MCP tool provider through 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 Zenserp. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Zenserp?"
)
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 Zenserp MCP Server
Connect your Zenserp account to any AI agent and harness the power of real-time search intelligence through natural conversation.
LlamaIndex agents combine Zenserp tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through 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
- 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 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 Zenserp to LlamaIndex via MCP
Follow these steps to integrate the Zenserp 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 10 tools from Zenserp
Why Use LlamaIndex with the Zenserp MCP Server
LlamaIndex provides unique advantages when paired with Zenserp through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Zenserp tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Zenserp tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Zenserp, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Zenserp tools were called, what data was returned, and how it influenced the final answer
Zenserp + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Zenserp MCP Server delivers measurable value.
Hybrid search: combine Zenserp real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Zenserp 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 Zenserp for fresh data
Analytical workflows: chain Zenserp queries with LlamaIndex's data connectors to build multi-source analytical reports
Zenserp MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Zenserp to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Zenserp to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpZenserp + LlamaIndex FAQ
Common questions about integrating Zenserp 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 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 LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.