How to Use the Lunatask MCP in LlamaIndex

Index your productivity data with LlamaIndex and the Lunatask MCP Server for grounded, context-aware AI responses.

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Connect Lunatask MCP to LlamaIndex

Create your Vinkius account to connect Lunatask to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Lunatask with LlamaIndex

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Index task metadata with LlamaIndex

Feed your task headers into a vector store to make your history searchable. By calling `list_tasks_metadata`, you populate your index with structural data. Your agent queries this index before executing `get_task_metadata` to find specific details. It provides answers grounded in your actual records.

Ground AI decisions in LlamaIndex

Use `list_notes_metadata` to build a searchable knowledge base of your journal history. Your agent references these headers to understand the context of your previous entries. When you ask about your progress, the system retrieves relevant items from the index. It performs targeted updates using `update_existing_task` based on that context.

Automate habit logging in LlamaIndex

Your agent tracks daily habits by querying the index for completion status. It identifies gaps and uses `track_habit_completion` to keep your records current. This creates a feedback loop where your history influences your future actions. The agent knows what needs to be done because it can read your past entries.

Setup guide

Set up Lunatask MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Lunatask MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

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

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Lunatask tools.",
)
response = await agent.run("List recent Lunatask data")

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Why Choose Vinkius

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Common questions about Lunatask MCP in LlamaIndex

Install the MCP tool spec, initialize the client, and convert the tools to a format the agent understands. This bridges the server to your index.

It indexes the metadata returned by the server. You can then run semantic searches against that data to find patterns in your work.

The server only provides metadata. Since your sensitive content is encrypted, the vector store only contains non-sensitive task and note headers.

You can include all eight tools in your agent spec. This allows the system to read, write, and track data as needed.

The agent performs a search, identifies the target task, and then calls the update tool. The index refreshes to reflect the new state.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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