How to Use the Structured MCP in LlamaIndex

Q: How do I find specific tasks using Structured and LlamaIndex?

You use listtasks to get the raw list. Indexing this output means you can later query your knowledge base, asking for a task title or start date without running the live API call again.

Q: Can I retrieve and index plan details using Structured and LlamaIndex?

Yes. Running getplandetails allows you to capture specific configuration data about a plan, which gets stored in your vector store for later semantic search.

Q: What is the best way to manage user context with Structured and LlamaIndex?

Run getuserprofile and ensure that output is included in your indexed resources. This makes all historical profile data searchable alongside your documents.

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Connect Structured MCP to LlamaIndex

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Setup Structured with LlamaIndex

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Vectorize structured tasks with LlamaIndex.

LlamaIndex turns API results into searchable knowledge. When you run `list_tasks`, the output isn't just a list; it becomes an indexed chunk of data. This lets your RAG application query past task lists for specific details later on.

Retrieve user context using LlamaIndex and MCP Server.

Need to know who the current user is? Use `get_user_profile`. The results of this tool call get indexed. You can then ask your agent questions like, 'What were the plans for User X last month?' and get an answer grounded in real API data.

Track plan history with LlamaIndex.

The `list_plans` tool is perfect for indexing. By saving every result into your vector store, you build a comprehensive record of all organizational plans. You'll never have to worry about hallucinating old data again.

Setup guide

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

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Common questions about Structured MCP in LlamaIndex

You use `list_tasks` to get the raw list. Indexing this output means you can later query your knowledge base, asking for a task title or start date without running the live API call again.

Yes. Running `get_plan_details` allows you to capture specific configuration data about a plan, which gets stored in your vector store for later semantic search.

Run `get_user_profile` and ensure that output is included in your indexed resources. This makes all historical profile data searchable alongside your documents.

This MCP Server handles structured plan, task, and user metadata. The primary sensitive data accessed is the user's profile information via `get_user_profile`.

You treat every tool call as a source of truth for your RAG system. Indexing structured data from tools like `list_plans` is the core mechanism that makes it useful.

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