Why can't the agent read my task names?

Lunatask uses end-to-end encryption. Task names and note bodies are encrypted on your device before being sent to the server. The API only has access to metadata like IDs and statuses.

Where do I find my Area ID?

In the Lunatask desktop app, go to an Area's settings. The unique Area ID (UUID) is displayed there for use in the API.

Can I track habits using this agent?

Yes, use the `track_habit_completion` tool with the unique habit ID to log a successful completion event.

Lunatask MCP Server for Pydantic AI 8 tools — connect in under 2 minutes

Name: Lunatask
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 8 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Lunatask 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 Lunatask "
            "(8 tools)."
        ),
    )

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

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 Lunatask MCP Server

Connect your Lunatask account to any AI agent to streamline your privacy-focused productivity. This MCP server enables your agent to create, update, and manage tasks, track habits, and log journal entries directly from natural language interfaces.

Pydantic AI validates every Lunatask tool response against typed schemas, catching data inconsistencies at build time. Connect 8 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

Task Creation — Add new tasks to specific Areas of Life with statuses like 'next' or 'later'
Habit Tracking — Log completions for your daily habits to stay consistent with your goals
Encrypted Journaling — Create secure, end-to-end encrypted journal entries directly from your conversation
Metadata Inspection — List all tasks and notes to monitor your productivity structure and statuses
Workflow Management — Update task priorities and move them through your personal workflow stages

Important Note on Privacy

Lunatask uses end-to-end encryption. While this API allows creating and updating content, it cannot read back the names or notes of your tasks once they are stored. The agent will only see technical metadata (IDs, dates, statuses).

The Lunatask MCP Server exposes 8 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 Lunatask to Pydantic AI via MCP

Follow these steps to integrate the Lunatask 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 8 tools from Lunatask with type-safe schemas

Why Use Pydantic AI with the Lunatask MCP Server

Pydantic AI provides unique advantages when paired with Lunatask 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 Lunatask integration code

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

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

Lunatask + Pydantic AI Use Cases

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

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

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

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

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

Lunatask MCP Tools for Pydantic AI (8)

These 8 tools become available when you connect Lunatask to Pydantic AI via MCP:

create_journal_entry

Add a new journal entry

create_new_task

Requires a name and an area_id. Create a new task

delete_task

Delete a task

get_task_metadata

Get metadata for a specific task

list_notes_metadata

List metadata for all notes

list_tasks_metadata

Note: Due to encryption, names and notes are not available via API. List metadata for all tasks

track_habit_completion

Log a completion for a habit

update_existing_task

Update an existing task

Example Prompts for Lunatask in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Lunatask immediately.

"List metadata for all my tasks in Lunatask."

"Track a completion for habit ID 'habit-123'."

"Create a new task named 'Review quarterly report' in area 'area-abc'."

Troubleshooting Lunatask MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Lunatask + Pydantic AI FAQ

Common questions about integrating Lunatask 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 Lunatask MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.