Do I need a paid plan to use the Structured programmatic API?

Yes. Programmatic integrations and the ability to systematically generate and query your personal API access require a Structured Pro plan to safely extract the required access tokens securely.

Can the integration delete past records destructively by accident executing commands natively?

Risk is inherently mitigated. The AI only calls mutations like `delete_task` precisely when instructed unequivocally. Records aren't inherently purged on exploration, allowing safe queries comprehensively continuously naturally.

Which platforms does the Structured integration support?

The MCP connects to the Structured Pro API, which syncs across iOS, iPadOS, macOS, and the web app. Any task created or updated via the AI is reflected on all your devices in real time.

Structured MCP Server for LlamaIndex 9 tools — connect in under 2 minutes

Name: Structured
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 9 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Structured as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

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python

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 Structured. "
            "You have 9 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Structured?"
    )
    print(response)

asyncio.run(main())

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About Structured MCP Server

Integrate the powerful tracking of the Structured daily planner directly into your conversational AI environment. Empower your productivity by allowing your LLM to intuitively create tasks, schedule complex recurring routines, and manage your day programmatically without opening the mobile app. With this MCP connector securely attached to your Structured Pro environment, your agent can serve as an active scheduling assistant, iterating dynamically through your agenda, parsing task structures, and executing adjustments organically.

LlamaIndex agents combine Structured tool responses with indexed documents for comprehensive, grounded answers. Connect 9 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

Agenda Discovery — Audit your scheduled events querying active records using list_tasks and retrieve deep metadata specific assignments utilizing get_task_details.
Task Orchestration — Drive agile agenda resolutions adding new items seamlessly executing create_task or adjusting timelines using update_task.
Routine Management — Check your active multi-step routines effectively through list_plans and isolate their specific structural constraints engaging get_plan_details.
Profile Validations — Safely extract your user metadata boundaries and operational statuses natively invoking get_user_profile.

The Structured MCP Server exposes 9 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 Structured to LlamaIndex via MCP

Follow these steps to integrate the Structured 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 9 tools from Structured

Why Use LlamaIndex with the Structured MCP Server

LlamaIndex provides unique advantages when paired with Structured through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Structured tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Structured tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Structured, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Structured tools were called, what data was returned, and how it influenced the final answer

Structured + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Structured MCP Server delivers measurable value.

Hybrid search: combine Structured real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Structured 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 Structured for fresh data

Analytical workflows: chain Structured queries with LlamaIndex's data connectors to build multi-source analytical reports

Structured MCP Tools for LlamaIndex (9)

These 9 tools become available when you connect Structured to LlamaIndex via MCP:

create_plan

Creates a new plan

create_task

Provide a title and optional start time. Creates a new task in Structured

delete_task

This action is irreversible. Permanently deletes a task

get_plan_details

Retrieves details for a specific plan

get_task_details

Retrieves details for a specific task

get_user_profile

Retrieves the current user profile

list_plans

Lists all structured plans

list_tasks

Lists all tasks in Structured

update_task

Updates an existing task

Example Prompts for Structured in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Structured immediately.

"Assess my active Structured environment, listing today's pending tasks, and mark the scheduled meeting block as successfully completed."

"List all active plans for the week, and display the detailed constraints of the 'Morning Focus' routine."

"Read my user profile cleanly to extract my workspace validation level and operational timezone."

Troubleshooting Structured MCP Server with LlamaIndex

Common issues when connecting Structured to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Structured + LlamaIndex FAQ

Common questions about integrating Structured MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Structured tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.