Can I get live match lineups?

Yes! Use the `get_fixture_lineup` tool with a fixture ID to get the full starting lineup, substitutes, and formations for both teams.

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

Name: GameScorekeeper
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 GameScorekeeper 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 GameScorekeeper "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in GameScorekeeper?"
    )
    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 GameScorekeeper MCP Server

Connect GameScorekeeper to your AI agent for real-time football (soccer) intelligence across major competitions.

Pydantic AI validates every GameScorekeeper 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

Competitions — Browse and list active competitions with detailed metadata
Fixtures — Get fixture schedules, live scores, and match results
Lineups — Access full match lineups with player positions and numbers
Team Intelligence — View team profiles, current form, and recent results
Player Stats — Access individual player statistics, career data, and performance metrics
Stage Navigation — Browse competition stages (group stage, knockouts, etc.)

The GameScorekeeper 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 GameScorekeeper to Pydantic AI via MCP

Follow these steps to integrate the GameScorekeeper 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 GameScorekeeper with type-safe schemas

Why Use Pydantic AI with the GameScorekeeper MCP Server

Pydantic AI provides unique advantages when paired with GameScorekeeper 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 GameScorekeeper integration code

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

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

GameScorekeeper + Pydantic AI Use Cases

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

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

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

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

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

GameScorekeeper MCP Tools for Pydantic AI (10)

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

get_competition_details

Get detailed info for a specific tournament

get_fixture_details

Get detailed match information

get_fixture_lineup

Get player lineups for a specific match

get_player_details

Get individual player profile

get_player_stats

Retrieve historical performance metrics for a player

get_team_details

Get basic information and logo for an esports team

get_team_form

Get recent performance form for a team

list_competition_stages

List stages (e.g., Playoffs, Group Stage) for a competition

list_competitions

List all supported esports tournaments and leagues

list_fixtures

List upcoming and past esports matches

Example Prompts for GameScorekeeper in Pydantic AI

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

"Show me the upcoming Champions League fixtures."

Troubleshooting GameScorekeeper MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

GameScorekeeper + Pydantic AI FAQ

Common questions about integrating GameScorekeeper 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 GameScorekeeper MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.