Lingyi Wanwu MCP Server for LlamaIndex 5 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Lingyi Wanwu 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 Lingyi Wanwu. "
"You have 5 tools available."
),
)
response = await agent.run(
"What tools are available in Lingyi Wanwu?"
)
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 Lingyi Wanwu MCP Server
Connect your AI agents to Lingyi Wanwu (01.AI), the high-performance AI lab founded by Dr. Kai-Fu Lee. This MCP provides 10 tools to automate interactions with the Yi series of large language models, including state-of-the-art chat completions, semantic embeddings, and account usage monitoring.
LlamaIndex agents combine Lingyi Wanwu tool responses with indexed documents for comprehensive, grounded answers. Connect 5 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
- Yi Model Interaction — Trigger chat completions with Yi-34B, Yi-Large, and other optimized models using persistent context
- Vector Embeddings — Generate high-dimensional semantic embeddings to power advanced RAG and search workflows
- Model Intelligence — List all available models and retrieve granular technical specifications for each version
- Account Management — Monitor your token consumption and balance programmatically to optimize costs
The Lingyi Wanwu MCP Server exposes 5 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 Lingyi Wanwu to LlamaIndex via MCP
Follow these steps to integrate the Lingyi Wanwu 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 5 tools from Lingyi Wanwu
Why Use LlamaIndex with the Lingyi Wanwu MCP Server
LlamaIndex provides unique advantages when paired with Lingyi Wanwu through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Lingyi Wanwu tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Lingyi Wanwu tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Lingyi Wanwu, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Lingyi Wanwu tools were called, what data was returned, and how it influenced the final answer
Lingyi Wanwu + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Lingyi Wanwu MCP Server delivers measurable value.
Hybrid search: combine Lingyi Wanwu real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Lingyi Wanwu 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 Lingyi Wanwu for fresh data
Analytical workflows: chain Lingyi Wanwu queries with LlamaIndex's data connectors to build multi-source analytical reports
Lingyi Wanwu MCP Tools for LlamaIndex (5)
These 5 tools become available when you connect Lingyi Wanwu to LlamaIndex via MCP:
chat_completions
Send a message to a Yi model
check_moderation
Check content for policy violations
get_embeddings
Generate text embeddings
get_usage
Retrieve account usage statistics
list_models
List available Yi models
Example Prompts for Lingyi Wanwu in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Lingyi Wanwu immediately.
"Chat with the Yi-Large model and ask 'Explain the impact of AI on the future of work'."
"Generate embeddings for my company's mission statement."
"Check my current account balance in Lingyi Wanwu."
Troubleshooting Lingyi Wanwu MCP Server with LlamaIndex
Common issues when connecting Lingyi Wanwu to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpLingyi Wanwu + LlamaIndex FAQ
Common questions about integrating Lingyi Wanwu 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 Lingyi Wanwu 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 Lingyi Wanwu to LlamaIndex
Get your token, paste the configuration, and start using 5 tools in under 2 minutes. No API key management needed.