Lanhu MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Lanhu as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 Lanhu. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Lanhu?"
)
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 Lanhu MCP Server
Empower your AI agent to orchestrate your product design workflow with Lanhu (蓝湖), the premier design collaboration platform for high-performance teams. By connecting Lanhu to your agent, you transform complex design handoffs and project coordination into a natural conversation. Your agent can instantly list your projects, retrieve design file information, audit layer structures, and even browse team comments without you needing to navigate the web interface. Whether you are managing a mobile app design or a large-scale enterprise system, your agent acts as a real-time design coordinator, keeping your assets organized and your production moving.
LlamaIndex agents combine Lanhu tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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
- Project Orchestration — List all accessible design projects and files across your Lanhu workspace.
- Design Auditing — Retrieve detailed metadata about design files, including layers and node structures.
- Collaboration Monitoring — Browse file comments and discussions to stay informed about team feedback.
- Board Management — Access design boards to understand project organization and milestones.
- Team Coordination — List teams and members to manage assignments and participation effectively.
The Lanhu MCP Server exposes 10 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 Lanhu to LlamaIndex via MCP
Follow these steps to integrate the Lanhu 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 10 tools from Lanhu
Why Use LlamaIndex with the Lanhu MCP Server
LlamaIndex provides unique advantages when paired with Lanhu through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Lanhu tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Lanhu tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Lanhu, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Lanhu tools were called, what data was returned, and how it influenced the final answer
Lanhu + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Lanhu MCP Server delivers measurable value.
Hybrid search: combine Lanhu real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Lanhu 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 Lanhu for fresh data
Analytical workflows: chain Lanhu queries with LlamaIndex's data connectors to build multi-source analytical reports
Lanhu MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Lanhu to LlamaIndex via MCP:
get_board
Get board details
get_comments
Get file comments
get_file
Get design file info
get_project
Get project details
list_boards
List project boards
list_layers
List file layers
list_members
List team members
list_project_files
g., from Sketch, Figma, XD) within a specific project. List project design files
list_team_projects
List team projects
list_teams
List all Lanhu teams
Example Prompts for Lanhu in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Lanhu immediately.
"List all my design projects on Lanhu."
"Show me the comments for design file 'checkout-v1'."
"List the layers for file 'homepage-main'."
Troubleshooting Lanhu MCP Server with LlamaIndex
Common issues when connecting Lanhu to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpLanhu + LlamaIndex FAQ
Common questions about integrating Lanhu 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 Lanhu 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 Lanhu to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.