How to Use the Mingdao Cloud MCP in LlamaIndex

Q: How do I get the latest data into LlamaIndex?

Configure your agent to trigger listrows as part of your refresh cycle. This pulls the most recent records from your worksheets to keep your vector store current.

Index your Mingdao Cloud worksheets into LlamaIndex for semantic search and grounded answers.

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Connect Mingdao Cloud MCP to LlamaIndex

Create your Vinkius account to connect Mingdao Cloud 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 Mingdao Cloud with LlamaIndex

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Semantic search across worksheets

Turn your raw worksheet records into a searchable knowledge base. The agent uses `list_rows` to extract content, which LlamaIndex then converts into vectors for retrieval. Stop guessing where your data lives. When you ask a question, the agent scans your indices, grounded by the actual, current values found in your worksheets.

Automated data retrieval for RAG

Feed your RAG application live data directly from your apps. By calling `get_row_details`, the agent pulls specific, high-fidelity records to answer complex queries about your business operations. This removes the need for static document uploads. Your knowledge base stays updated because the agent refreshes the index by periodically calling `list_rows`.

Schema-driven knowledge mapping

Understand the relationships between your data points by reading schemas first. Using `get_worksheet_schema`, the agent builds a map of your fields before it starts indexing rows. This ensures the semantic search understands the context of each column. Your index becomes a structured reflection of your actual business logic, not just a flat list of text.

Setup guide

Set up Mingdao Cloud 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 Mingdao Cloud 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 Mingdao Cloud tools.",
)
response = await agent.run("List recent Mingdao Cloud data")

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Common questions about Mingdao Cloud MCP in LlamaIndex

It uses the MCP tools to query your worksheets. The agent fetches rows and indexes them so you can perform semantic searches on your business data.

Yes. Once the data is indexed, you ask natural language questions. LlamaIndex retrieves the relevant row data to provide grounded, accurate answers.

We treat your worksheet records as private, transient data. The server only accesses what you explicitly authorize through the tool list, and it doesn't store your credentials.

Configure your agent to trigger `list_rows` as part of your refresh cycle. This pulls the most recent records from your worksheets to keep your vector store current.

Only the tools you include are exposed. You control access to specific worksheets, keeping sensitive record data restricted to exactly what the agent requires for indexing.

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