How to integrate WaniKani MCP with Pydantic AI

This guide walks you through connecting WaniKani to Pydantic AI using the Composio tool router. By the end, you'll have a working WaniKani agent that can view your reviews, check your assignments, track level progress, and add study notes through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a WaniKani account through Composio's WaniKani MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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WaniKani is a spaced-repetition platform for learning Japanese radicals, kanji, and vocabulary. It helps learners build long-term retention with structured lessons, reviews, and progress tracking.

16 Tools

Introduction

This guide walks you through connecting WaniKani to Pydantic AI using the Composio tool router. By the end, you'll have a working WaniKani agent that can view your reviews, check your assignments, track level progress, and add study notes through natural language commands.

This guide will help you understand how to give your Pydantic AI agent real control over a WaniKani account through Composio's WaniKani MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate WaniKani with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for WaniKani
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your WaniKani workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

What is the WaniKani MCP server, and what's possible with it?

The WaniKani MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your WaniKani account. It provides structured and secure access so your agent can perform WaniKani operations on your behalf.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account with an active API key
  • Basic familiarity with Python and async programming
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

bash
pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like WaniKani
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs
5

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to WaniKani
  • MCPServerStreamableHTTP connects to the WaniKani MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for WaniKani
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["wanikani"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to WaniKani tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
wanikani_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[wanikani_mcp],
    instructions=(
        "You are a WaniKani assistant. Use WaniKani tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the WaniKani endpoint
  • The agent uses GPT-5 to interpret user commands and perform WaniKani operations
  • The instructions field defines the agent's role and behavior
8

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with WaniKani.\n")

while True:
    user_input = input("You: ").strip()
    if user_input.lower() in {"exit", "quit", "bye"}:
        print("\nGoodbye!")
        break
    if not user_input:
        continue

    print("\nAgent is thinking...\n", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • WaniKani API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with WaniKani and Pydantic AI:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for WaniKani
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["wanikani"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    wanikani_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[wanikani_mcp],
        instructions=(
            "You are a WaniKani assistant. Use WaniKani tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with WaniKani.\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "bye"}:
            print("\nGoodbye!")
            break
        if not user_input:
            continue

        print("\nAgent is thinking...\n", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've built a Pydantic AI agent that can interact with WaniKani through Composio's Tool Router. With this setup, your agent can perform real WaniKani actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + WaniKani for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
TOOLS

Supported Tools

Every WaniKani action and event your agent gets out of the box.

Create Study Material

Create the sole study-material record for a subject, optionally with notes or meaning synonyms.

Get Assignments

Retrieve one assignment by ID, or list the connected user's assignments using progress, availability, level, subject, and update filters.

Get Level Progressions

Retrieve one level-progression record by ID, or list the user's level history with ID and update-time filters.

Get Resets

Retrieve one account-reset record by ID, or list account level-reset history.

Get Reviews

Retrieve one completed review by ID, or list completed reviews filtered by review IDs, subject IDs, or update time.

Get Review Statistics

Retrieve one per-subject review statistic by ID, or list answer totals, streaks, and accuracy using subject and percentage filters.

Get Spaced Repetition Systems

Retrieve one spaced-repetition system by ID, or list SRS definitions and their stage intervals.

Get Study Materials

Retrieve one study-material record by ID, or list the user's subject notes and meaning synonyms with subject and update filters.

Get Study Summary

Get lessons available now and review availability over the next 24 hours for the connected user.

Get Subjects

Retrieve one WaniKani subject by ID, or search radicals, kanji, vocabulary, and kana vocabulary by IDs, types, slugs, levels, visibility, or update time.

Get User

Get the connected user's identity, level, subscription status, vacation state, and application preferences.

Get Voice Actors

Retrieve one WaniKani pronunciation voice actor by ID, or list voice actors with ID and update-time filters.

Start Assignment

Irreversibly mark one unlocked lesson assignment as started and move it into reviews.

Submit Review

Irreversibly submit a completed review, changing assignment progress and review statistics.

Update Study Material

Update at least one note or meaning-synonym field on an existing study-material record without changing its subject.

Update User Preferences

Update one or more supported WaniKani lesson or review preferences.

FRAMEWORKS

How to build WaniKani MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use WaniKani MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use WaniKani MCP with Claude Agent SDK

Claude Code logo

Claude Code

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Claude Cowork logo

Claude Cowork

Use WaniKani MCP with Claude Cowork

Codex logo

Codex

Use WaniKani MCP with Codex

OpenClaw logo

OpenClaw

Use WaniKani MCP with OpenClaw

Hermes logo

Hermes

Use WaniKani MCP with Hermes

CLI logo

CLI

Use WaniKani MCP with CLI

Google ADK logo

Google ADK

Use WaniKani MCP with Google ADK

LangChain logo

LangChain

Use WaniKani MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use WaniKani MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use WaniKani MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use WaniKani MCP with LlamaIndex

CrewAI logo

CrewAI

Use WaniKani MCP with CrewAI

FAQ

Frequently asked questions

With a standalone WaniKani MCP server, the agents and LLMs can only access a fixed set of WaniKani tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from WaniKani and many other apps based on the task at hand, all through a single MCP endpoint.

Yes, you can. Pydantic AI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right WaniKani tools.

Yes, absolutely. You can configure which WaniKani scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your WaniKani data and credentials are handled as safely as possible.

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Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati
Harsha Gaddipati
Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

Abhi Arya
Abhi Arya
Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave
Nirman Dave
CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu
Ryan Yu
Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

Tomisin Jenrola
Tomisin Jenrola
Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Jerome Leclanche
Jerome Leclanche
Co-Founder, Ingram Technologies