NVIDIA AI MCP Server for LlamaIndex 9 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add NVIDIA AI as an MCP tool provider through the 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 NVIDIA AI. "
"You have 9 tools available."
),
)
response = await agent.run(
"What tools are available in NVIDIA AI?"
)
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 NVIDIA AI MCP Server
Connect NVIDIA AI to any AI agent and harness the power of GPU-accelerated foundation models — chat with Llama, generate embeddings, write code with CodeLlama, translate text, and perform complex reasoning through the NVIDIA API Catalog.
LlamaIndex agents combine NVIDIA AI tool responses with indexed documents for comprehensive, grounded answers. Connect 9 tools through the 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
- Chat with LLMs — Access Llama 3.1, Mistral, Nemotron, and more via chat completions
- Generate Embeddings — Create vector embeddings for search and clustering
- Code Generation — Write code from natural language prompts using CodeLlama
- Summarization — Condense long documents into concise summaries
- Translation — Neural translation between dozens of languages
- Text-to-SQL — Convert natural language questions into SQL queries
- Sentiment Analysis — Analyze the emotional tone of text
- Complex Reasoning — Ask questions to the 405B-parameter reasoning model
The NVIDIA AI MCP Server exposes 9 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 NVIDIA AI to LlamaIndex via MCP
Follow these steps to integrate the NVIDIA AI 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 9 tools from NVIDIA AI
Why Use LlamaIndex with the NVIDIA AI MCP Server
LlamaIndex provides unique advantages when paired with NVIDIA AI through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine NVIDIA AI tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain NVIDIA AI tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query NVIDIA AI, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what NVIDIA AI tools were called, what data was returned, and how it influenced the final answer
NVIDIA AI + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the NVIDIA AI MCP Server delivers measurable value.
Hybrid search: combine NVIDIA AI real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query NVIDIA AI 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 NVIDIA AI for fresh data
Analytical workflows: chain NVIDIA AI queries with LlamaIndex's data connectors to build multi-source analytical reports
NVIDIA AI MCP Tools for LlamaIndex (9)
These 9 tools become available when you connect NVIDIA AI to LlamaIndex via MCP:
analyze_sentiment
Analyze the sentiment of a text
ask_question
Optionally provide context for better answers. Ask a question to a powerful reasoning model (405B params)
chat_completion
Use "model" to specify which AI model (e.g., "meta/llama-3.1-70b-instruct", "mistralai/mistral-large"). Messages should be in OpenAI format: [{role: "user", content: "..."}]. Chat with an NVIDIA AI model (Llama, Mistral, etc)
generate_code
Specify language if needed. Generate code from a natural language prompt
get_embeddings
Model: "nvidia/nv-embed-v1". Generate vector embeddings from text
list_models
List all available AI models on the NVIDIA API Catalog
summarize_text
Summarize long text into a concise version
text_to_sql
Convert natural language to SQL query
translate_text
Translate text to another language
Example Prompts for NVIDIA AI in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with NVIDIA AI immediately.
"Generate Python code for a REST API with FastAPI."
"Translate 'Hello, how are you?' to Japanese."
"Summarize: The quarterly report shows revenue grew 15% YoY..."
Troubleshooting NVIDIA AI MCP Server with LlamaIndex
Common issues when connecting NVIDIA AI to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpNVIDIA AI + LlamaIndex FAQ
Common questions about integrating NVIDIA AI 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 NVIDIA AI 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 NVIDIA AI to LlamaIndex
Get your token, paste the configuration, and start using 9 tools in under 2 minutes. No API key management needed.