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 CrewAI 8 tools — connect in under 2 minutes

Name: Lunatask
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 8 Tools Framework

Connect your CrewAI agents to Lunatask through Vinkius, pass the Edge URL in the `mcps` parameter and every Lunatask tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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

from crewai import Agent, Task, Crew

agent = Agent(
    role="Lunatask Specialist",
    goal="Help users interact with Lunatask effectively",
    backstory=(
        "You are an expert at leveraging Lunatask tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Lunatask "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 8 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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.

When paired with CrewAI, Lunatask becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Lunatask tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

Follow these steps to integrate the Lunatask MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 8 tools from Lunatask

Why Use CrewAI with the Lunatask MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Lunatask through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Lunatask + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Lunatask for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Lunatask, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Lunatask tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Lunatask against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Lunatask MCP Tools for CrewAI (8)

These 8 tools become available when you connect Lunatask to CrewAI 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 CrewAI

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

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Lunatask + CrewAI FAQ

Common questions about integrating Lunatask MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.