IBM Quantum MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add IBM Quantum 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 IBM Quantum. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in IBM Quantum?"
)
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 IBM Quantum MCP Server
Connect IBM Quantum to any AI agent via MCP.
How to Connect IBM Quantum to LlamaIndex via MCP
Follow these steps to integrate the IBM Quantum 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 8 tools from IBM Quantum
Why Use LlamaIndex with the IBM Quantum MCP Server
LlamaIndex provides unique advantages when paired with IBM Quantum through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine IBM Quantum tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain IBM Quantum tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query IBM Quantum, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what IBM Quantum tools were called, what data was returned, and how it influenced the final answer
IBM Quantum + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the IBM Quantum MCP Server delivers measurable value.
Hybrid search: combine IBM Quantum real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query IBM Quantum 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 IBM Quantum for fresh data
Analytical workflows: chain IBM Quantum queries with LlamaIndex's data connectors to build multi-source analytical reports
IBM Quantum MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect IBM Quantum to LlamaIndex via MCP:
cancel_job
Cancel a quantum job
get_backend_details
Get details for a specific quantum backend
get_job_details
Get details for a specific quantum job
get_job_result
Get the result of a quantum job
list_backends
List available quantum backends (devices)
list_jobs
List quantum jobs
list_providers
List IBM Quantum providers
submit_job
Submit a quantum job
Troubleshooting IBM Quantum MCP Server with LlamaIndex
Common issues when connecting IBM Quantum to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpIBM Quantum + LlamaIndex FAQ
Common questions about integrating IBM Quantum 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 IBM Quantum 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 IBM Quantum to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.