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 LlamaIndex 8 tools — connect in under 2 minutes

Name: Lunatask
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 8 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Lunatask as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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 llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Lunatask. "
            "You have 8 tools available."
        ),
    )

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

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 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.

LlamaIndex agents combine Lunatask tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

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 LlamaIndex 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 LlamaIndex via MCP

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

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

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

Why Use LlamaIndex with the Lunatask MCP Server

LlamaIndex provides unique advantages when paired with Lunatask through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Lunatask tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Lunatask tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Lunatask, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Lunatask tools were called, what data was returned, and how it influenced the final answer

Lunatask + LlamaIndex Use Cases

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

Hybrid search: combine Lunatask real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Lunatask to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Lunatask for fresh data

Analytical workflows: chain Lunatask queries with LlamaIndex's data connectors to build multi-source analytical reports

Lunatask MCP Tools for LlamaIndex (8)

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

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

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Lunatask + LlamaIndex FAQ

Common questions about integrating Lunatask MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Lunatask tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.