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 Pydantic AI 9 tools — connect in under 2 minutes

Name: Structured
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 9 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Structured through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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 pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Structured "
            "(9 tools)."
        ),
    )

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

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.

Pydantic AI validates every Structured tool response against typed schemas, catching data inconsistencies at build time. Connect 9 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

Follow these steps to integrate the Structured MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

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 with type-safe schemas

Why Use Pydantic AI with the Structured MCP Server

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

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Structured integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Structured connection logic from agent behavior for testable, maintainable code

Structured + Pydantic AI Use Cases

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

Type-safe data pipelines: query Structured with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Structured tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Structured and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Structured responses and write comprehensive agent tests

Structured MCP Tools for Pydantic AI (9)

These 9 tools become available when you connect Structured to Pydantic AI 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 Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Structured + Pydantic AI FAQ

Common questions about integrating Structured MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Structured MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.