SERPHouse MCP Server for LlamaIndex 11 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add SERPHouse 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 SERPHouse. "
"You have 11 tools available."
),
)
response = await agent.run(
"What tools are available in SERPHouse?"
)
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 SERPHouse MCP Server
Equip your AI assistant with the ultimate crawler module engineered for unbridled search engine access. The SERPHouse MCP integration overcomes the constraints of offline AI knowledge by granting direct, proxy-rotated querying power over Google and Bing endpoints. Seamlessly transform your agent into a high-performance web discovery engine without triggering Captcha blocks.
LlamaIndex agents combine SERPHouse tool responses with indexed documents for comprehensive, grounded answers. Connect 11 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.
The SERPHouse MCP Server exposes 11 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 SERPHouse to LlamaIndex via MCP
Follow these steps to integrate the SERPHouse 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 11 tools from SERPHouse
Why Use LlamaIndex with the SERPHouse MCP Server
LlamaIndex provides unique advantages when paired with SERPHouse through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine SERPHouse tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain SERPHouse tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query SERPHouse, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what SERPHouse tools were called, what data was returned, and how it influenced the final answer
SERPHouse + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the SERPHouse MCP Server delivers measurable value.
Hybrid search: combine SERPHouse real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query SERPHouse 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 SERPHouse for fresh data
Analytical workflows: chain SERPHouse queries with LlamaIndex's data connectors to build multi-source analytical reports
SERPHouse MCP Tools for LlamaIndex (11)
These 11 tools become available when you connect SERPHouse to LlamaIndex via MCP:
bing_images
Searches for images on Bing
bing_news
Searches for news on Bing
bing_search
Performs a search on Bing
get_account_info
Retrieves SERPHouse account information
google_images
Searches for images on Google
google_news
Searches for news on Google
google_scholar
Searches for scholarly articles on Google Scholar
google_search
Supports advanced parameters like "location" and "lang". Performs a search on Google
google_shopping
Searches for products on Google Shopping
google_videos
Searches for videos on Google
list_locations
Lists supported locations for SERP queries
Example Prompts for SERPHouse in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with SERPHouse immediately.
"Run a targeted Google Scholar query aimed at finding recent papers on 'quantum consciousness theory' focusing on latest available years."
"Search Bing News locally from a generic 'United Kingdom' location tag to see the latest headlines on economic metrics."
Troubleshooting SERPHouse MCP Server with LlamaIndex
Common issues when connecting SERPHouse to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpSERPHouse + LlamaIndex FAQ
Common questions about integrating SERPHouse 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 SERPHouse 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 SERPHouse to LlamaIndex
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.