Linkup (AI Search & RAG) MCP Server for LlamaIndex 2 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Linkup (AI Search & RAG) 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 Linkup (AI Search & RAG). "
"You have 2 tools available."
),
)
response = await agent.run(
"What tools are available in Linkup (AI Search & RAG)?"
)
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 Linkup (AI Search & RAG) MCP Server
Connect your Linkup account to any AI agent and take full control of real-time web intelligence and content retrieval for RAG pipelines through natural conversation.
LlamaIndex agents combine Linkup (AI Search & RAG) tool responses with indexed documents for comprehensive, grounded answers. Connect 2 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
- Semantic Web Search — Execute context-rich queries that return high-relevancy results specifically optimized for Large Language Models directly from your agent
- Deep Content Retrieval — Extract clean, readable text from any web URL, stripping away noise and navigation to feed high-quality grounding data to your AI
- RAG-Ready Payloads — Retrieve structured search results including titles, snippets, and source URLs designed for seamless integration into vector stores
- Precision Extraction — Target specific URLs for content parsing, ensuring your agent has the exact technical context or documentation required for its task
- Real-time Intelligence — Access the latest facts and data from across the internet to ground your agent's answers in up-to-date reality
- Search Breadth — Switch between standard and deep search modes to balance between rapid fact-finding and comprehensive research across the web
The Linkup (AI Search & RAG) MCP Server exposes 2 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 Linkup (AI Search & RAG) to LlamaIndex via MCP
Follow these steps to integrate the Linkup (AI Search & RAG) 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 2 tools from Linkup (AI Search & RAG)
Why Use LlamaIndex with the Linkup (AI Search & RAG) MCP Server
LlamaIndex provides unique advantages when paired with Linkup (AI Search & RAG) through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Linkup (AI Search & RAG) tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Linkup (AI Search & RAG) tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Linkup (AI Search & RAG), a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Linkup (AI Search & RAG) tools were called, what data was returned, and how it influenced the final answer
Linkup (AI Search & RAG) + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Linkup (AI Search & RAG) MCP Server delivers measurable value.
Hybrid search: combine Linkup (AI Search & RAG) real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Linkup (AI Search & RAG) 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 Linkup (AI Search & RAG) for fresh data
Analytical workflows: chain Linkup (AI Search & RAG) queries with LlamaIndex's data connectors to build multi-source analytical reports
Linkup (AI Search & RAG) MCP Tools for LlamaIndex (2)
These 2 tools become available when you connect Linkup (AI Search & RAG) to LlamaIndex via MCP:
fetch_url
Bypasses advanced bot protections executing complex SPA JavaScript loops automatically. Fetch and extract clean content from any specific URL using Linkup Platform
search_web
Choose "fast" mapping for basic factual requests and "deep" for thorough research limits. Perform a real-time web search extracting deep answers utilizing Linkup Platform
Example Prompts for Linkup (AI Search & RAG) in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Linkup (AI Search & RAG) immediately.
"Search for the latest NVIDIA earnings report summary"
"Extract the technical specifications from this documentation URL: [url]"
"Deep search for 'AI agent security best practices 2024'"
Troubleshooting Linkup (AI Search & RAG) MCP Server with LlamaIndex
Common issues when connecting Linkup (AI Search & RAG) to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpLinkup (AI Search & RAG) + LlamaIndex FAQ
Common questions about integrating Linkup (AI Search & RAG) 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 Linkup (AI Search & RAG) 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 Linkup (AI Search & RAG) to LlamaIndex
Get your token, paste the configuration, and start using 2 tools in under 2 minutes. No API key management needed.