HowLongToBeat MCP Server for Pydantic AI 1 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect HowLongToBeat through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
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 HowLongToBeat "
"(1 tools)."
),
)
result = await agent.run(
"What tools are available in HowLongToBeat?"
)
print(result.data)
asyncio.run(main())
* 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 HowLongToBeat MCP Server
Equip your AI agent with the ultimate gaming library intelligence via the HowLongToBeat MCP server. This integration provides instant access to the world's most trusted source for game completion times. Your agent can search for any video game and retrieve precise timing data for the 'Main Story', 'Main + Extra', and 'Completionist' runs. Whether you're planning your backlog, deciding on your next purchase, or auditing your play style, your agent acts as a dedicated gaming advisor through natural conversation.
Pydantic AI validates every HowLongToBeat tool response against typed schemas, catching data inconsistencies at build time. Connect 1 tools through the 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
- Game Time Search — Find how long it takes to beat any video game.
- Playstyle Comparison — Compare durations for different completion levels (story vs. 100%).
- Release Intelligence — Retrieve world release dates and exact game titles for thousands of entries.
- Backlog Auditing — Summarize expected playtimes for entire lists of games.
The HowLongToBeat MCP Server exposes 1 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 HowLongToBeat to Pydantic AI via MCP
Follow these steps to integrate the HowLongToBeat 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 1 tools from HowLongToBeat with type-safe schemas
Why Use Pydantic AI with the HowLongToBeat MCP Server
Pydantic AI provides unique advantages when paired with HowLongToBeat 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 HowLongToBeat integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your HowLongToBeat connection logic from agent behavior for testable, maintainable code
HowLongToBeat + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the HowLongToBeat MCP Server delivers measurable value.
Type-safe data pipelines: query HowLongToBeat with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple HowLongToBeat tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query HowLongToBeat and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock HowLongToBeat responses and write comprehensive agent tests
HowLongToBeat MCP Tools for Pydantic AI (1)
These 1 tools become available when you connect HowLongToBeat to Pydantic AI via MCP:
search_game_times
Search for game completion times
Example Prompts for HowLongToBeat in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with HowLongToBeat immediately.
"How long does it take to beat the main story of The Witcher 3?"
"Is 'Hades' a short game for a completionist?"
"Compare the completion times for 'Skyrim' and 'Starfield'."
Troubleshooting HowLongToBeat MCP Server with Pydantic AI
Common issues when connecting HowLongToBeat to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiHowLongToBeat + Pydantic AI FAQ
Common questions about integrating HowLongToBeat MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
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?
Can I switch LLM providers without changing MCP code?
Connect HowLongToBeat with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect HowLongToBeat to Pydantic AI
Get your token, paste the configuration, and start using 1 tools in under 2 minutes. No API key management needed.