How to Use the Lanhu MCP in LlamaIndex

Q: Will Lanhu and LlamaIndex work with large projects?

They will. You can paginate through listteams and listteamprojects to ingest large amounts of data without overloading your memory.

Index your design history with the Lanhu MCP Server and LlamaIndex for grounded AI answers.

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Connect Lanhu MCP to LlamaIndex

Create your Vinkius account to connect Lanhu to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Lanhu with LlamaIndex

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Claude

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Create a searchable design index

Turn your design documentation into a queryable knowledge base. Run `list_boards` and `list_project_files` to gather project context, then index those results into your vector store. Your agent no longer guesses about design specs. It queries the index to find exact layer details retrieved via `list_layers`, ensuring the answers it gives are grounded in your actual project files.

Ground AI in live design data

Stop worrying about hallucinations in your design handoff process. By integrating this MCP Server, your agent pulls real-time board layouts using `get_board` and matches them against your codebase. This creates a unified source of truth. When you ask about specific project requirements, the agent fetches the latest data from `get_project` before providing an answer.

Query past design feedback

Audit previous design decisions by searching your comment history. The agent uses `get_comments` to extract feedback threads, which you then store in your index for future reference. This makes institutional knowledge accessible. You can ask your agent what was decided about a specific feature, and it retrieves the exact comment thread from Lanhu.

Setup guide

Set up Lanhu MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Lanhu MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Lanhu tools.",
)
response = await agent.run("List recent Lanhu data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Lanhu. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Lanhu MCP in LlamaIndex

Yes. You index the tool output into a vector store. This allows you to perform semantic searches across your design files and project metadata.

The server converts tool results into documents for your index. You then use standard retrieval patterns to find relevant design info based on your query.

They will. You can paginate through `list_teams` and `list_team_projects` to ingest large amounts of data without overloading your memory.

It stays on the Lanhu servers. Only the metadata you choose to index is stored in your local vector store, protected by your existing access controls.

Implement a periodic refresh trigger in your application. The agent calls `list_project_files` to check for updates and updates your index with any new asset versions.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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