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 LangChain 9 tools — connect in under 2 minutes

Name: Structured
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 9 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Structured through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "structured": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Structured, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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* 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 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.

LangChain's ecosystem of 500+ components combines seamlessly with Structured through native MCP adapters. Connect 9 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

Follow these steps to integrate the Structured MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 9 tools from Structured via MCP

Why Use LangChain with the Structured MCP Server

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

The largest ecosystem of integrations, chains, and agents. combine Structured MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Structured queries for multi-turn workflows

Structured + LangChain Use Cases

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

RAG with live data: combine Structured tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Structured, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Structured tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Structured tool call, measure latency, and optimize your agent's performance

Structured MCP Tools for LangChain (9)

These 9 tools become available when you connect Structured to LangChain 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 LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Structured + LangChain FAQ

Common questions about integrating Structured MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.