Lunatask MCP Server for OpenAI Agents SDK 8 tools — connect in under 2 minutes
The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Lunatask through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MCPServerStreamableHttp(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
) as mcp_server:
agent = Agent(
name="Lunatask Assistant",
instructions=(
"You help users interact with Lunatask. "
"You have access to 8 tools."
),
mcp_servers=[mcp_server],
)
result = await Runner.run(
agent, "List all available tools from Lunatask"
)
print(result.final_output)
asyncio.run(main())
* 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.
The OpenAI Agents SDK auto-discovers all 8 tools from Lunatask through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Lunatask, another analyzes results, and a third generates reports, all orchestrated through Vinkius.
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 OpenAI Agents SDK 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 OpenAI Agents SDK via MCP
Follow these steps to integrate the Lunatask MCP Server with OpenAI Agents SDK.
Install the SDK
Run pip install openai-agents in your Python environment
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
Run the script
Save the code above and run it: python agent.py
Explore tools
The agent will automatically discover 8 tools from Lunatask
Why Use OpenAI Agents SDK with the Lunatask MCP Server
OpenAI Agents SDK provides unique advantages when paired with Lunatask through the Model Context Protocol.
Native MCP integration via `MCPServerSse`, pass the URL and the SDK auto-discovers all tools with full type safety
Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure
Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate
First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output
Lunatask + OpenAI Agents SDK Use Cases
Practical scenarios where OpenAI Agents SDK combined with the Lunatask MCP Server delivers measurable value.
Automated workflows: build agents that query Lunatask, process the data, and trigger follow-up actions autonomously
Multi-agent orchestration: create specialist agents. one queries Lunatask, another analyzes results, a third generates reports
Data enrichment pipelines: stream data through Lunatask tools and transform it with OpenAI models in a single async loop
Customer support bots: agents query Lunatask to resolve tickets, look up records, and update statuses without human intervention
Lunatask MCP Tools for OpenAI Agents SDK (8)
These 8 tools become available when you connect Lunatask to OpenAI Agents SDK 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 OpenAI Agents SDK
Ready-to-use prompts you can give your OpenAI Agents SDK 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 OpenAI Agents SDK
Common issues when connecting Lunatask to OpenAI Agents SDK through the Vinkius, and how to resolve them.
MCPServerStreamableHttp not found
pip install --upgrade openai-agentsAgent not calling tools
Lunatask + OpenAI Agents SDK FAQ
Common questions about integrating Lunatask MCP Server with OpenAI Agents SDK.
How does the OpenAI Agents SDK connect to MCP?
MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.Can I use multiple MCP servers in one agent?
MCPServerSse instances to the agent constructor. The agent can use tools from all connected servers within a single run.Does the SDK support streaming responses?
Connect Lunatask with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Lunatask to OpenAI Agents SDK
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.