How to Use the Lunatask MCP in Pydantic AI

Q: Does Pydantic AI allow access to my Lunatask notes?

It can list metadata via listnotesmetadata, but the actual note content is never returned by the server. Your privacy remains intact.

Use Pydantic AI to validate every interaction with Lunatask, ensuring your agent actions always match your schema.

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Connect Lunatask MCP to Pydantic AI

Create your Vinkius account to connect Lunatask to Pydantic AI 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 Pydantic AI

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Perplexity

Type-safe task management for Pydantic AI

Every call to `create_new_task` or `update_existing_task` is validated against your Pydantic models. If the data doesn't match the expected format, the agent stops before sending a bad request. This prevents silent failures in your agent workflow. You get immediate feedback if your task updates are malformed.

Rigorous habit tracking validation

Your agent uses `track_habit_completion` to log your habits. Pydantic AI ensures the input parameters are correct every single time. This removes the risk of hallucinated fields. Your agent interacts with your habit list using strict, typed definitions.

Metadata validation for secure context

The server uses `list_tasks_metadata` and `get_task_metadata` to give your agent the necessary context. Pydantic AI enforces that the returned metadata structure matches your expectations. This keeps your agent logic predictable. You rely on metadata to organize your day while keeping sensitive content locked away.

Setup guide

Set up Lunatask MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "lunatask-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Lunatask tools.",
)

result = await agent.run("List recent Lunatask transactions")
print(result.output)

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Common questions about Lunatask MCP in Pydantic AI

It guarantees that every tool input and output matches your defined types. This eliminates runtime errors when your agent calls `create_journal_entry`.

It can list metadata via `list_notes_metadata`, but the actual note content is never returned by the server. Your privacy remains intact.

Yes, the tools are designed to work with Pydantic models. You can define your own schemas to handle the server's responses safely.

Pydantic AI will catch the validation error before the agent executes the `update_existing_task` tool. You get a clean error message instead of an API failure.

The server's design inherently limits exposure. By using Pydantic AI to validate only the metadata fields, you ensure no raw content enters your agent's memory.

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