How do I find my Habitify API Key?

Open the Habitify mobile app, go to **Settings > API Credentials** (or Settings > Account > API), and tap **Generate New Key**. Note that API access may require a premium subscription.

Can I record progress for my habits via the AI agent?

Yes! Use the `add_habit_log` tool. You can specify the habit ID and the value you want to record (e.g., '10 reps' or '30 minutes').

Does the integration support viewing statistics?

Yes, you can use the `get_habit_stats` tool to retrieve performance data for any habit within a specific date range, helping you analyze your consistency.

Can I create new habits through this integration?

Yes! The `create_habit` tool allows you to add new habits to your Habitify account directly from your conversational interface.

Habitify MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Habitify
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Habitify through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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 pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Habitify "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Habitify?"
    )
    print(result.data)

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 Habitify MCP Server

Connect your Habitify account to any AI agent and take full control of your personal growth and habit-tracking workflows through natural conversation.

Pydantic AI validates every Habitify tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Habit Oversight — List all habits you are tracking and retrieve detailed information for each.
Journal Monitoring — Get a daily overview of your completion status and progress for any specific date.
Log Management — Record progress for your habits (reps, minutes, etc.) and view history logs efficiently.
Statistical Insights — Retrieve performance statistics for any habit within a custom date range.
Personalized Growth — Create new habits or update existing ones directly from your chat or IDE.
Area Categorization — Organize and browse your habits by areas of focus like Health, Work, or Mindset.

The Habitify MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI 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 Habitify to Pydantic AI via MCP

Follow these steps to integrate the Habitify MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

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 10 tools from Habitify with type-safe schemas

Why Use Pydantic AI with the Habitify MCP Server

Pydantic AI provides unique advantages when paired with Habitify through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Habitify integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Habitify connection logic from agent behavior for testable, maintainable code

Habitify + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Habitify MCP Server delivers measurable value.

Type-safe data pipelines: query Habitify with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Habitify tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Habitify and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Habitify responses and write comprehensive agent tests

Habitify MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Habitify to Pydantic AI via MCP:

add_habit_log

g., number of reps, minutes, or completion) to a habit. Record progress for a specific habit

create_habit

Create a new habit to track

delete_habit

Permanently delete a habit

get_habit

Get detailed information about a specific habit

get_habit_stats

Get statistics for a habit within a date range

get_journal

Get habits with completion status for a specific date

list_areas

List all habit areas (categories)

list_habit_logs

List all logs for a specific habit

list_habits

List all habits in your Habitify account

update_habit

Update an existing habit details

Example Prompts for Habitify in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Habitify immediately.

"What habits do I need to complete today?"

"Log 30 minutes of reading for today."

"Show me my stats for 'Morning Meditation' from last week."

Troubleshooting Habitify MCP Server with Pydantic AI

Common issues when connecting Habitify to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Habitify + Pydantic AI FAQ

Common questions about integrating Habitify MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Habitify MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.