SportsDB MCP Server for Pydantic AI 12 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect SportsDB through 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 SportsDB "
"(12 tools)."
),
)
result = await agent.run(
"What tools are available in SportsDB?"
)
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 SportsDB MCP Server
Connect SportsDB (sportdb.dev) to your AI agent for comprehensive sports intelligence across hundreds of leagues worldwide.
Pydantic AI validates every SportsDB tool response against typed schemas, catching data inconsistencies at build time. Connect 12 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
- Team & Player Search — Find teams and players by name across all sports and leagues
- Event Search — Search for matches and events by name or keyword
- League Navigation — Browse all sports, leagues, and countries in the database
- Match Tracking — Check upcoming fixtures and recent results for any team or league
- League Tables — Access current standings with points, goals, and form data
- Team & Player Profiles — Detailed info including badges, venues, and career stats
The SportsDB MCP Server exposes 12 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 SportsDB to Pydantic AI via MCP
Follow these steps to integrate the SportsDB 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 12 tools from SportsDB with type-safe schemas
Why Use Pydantic AI with the SportsDB MCP Server
Pydantic AI provides unique advantages when paired with SportsDB 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 SportsDB integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your SportsDB connection logic from agent behavior for testable, maintainable code
SportsDB + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the SportsDB MCP Server delivers measurable value.
Type-safe data pipelines: query SportsDB with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple SportsDB tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query SportsDB and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock SportsDB responses and write comprehensive agent tests
SportsDB MCP Tools for Pydantic AI (12)
These 12 tools become available when you connect SportsDB to Pydantic AI via MCP:
get_last_events
Get last results for a team
get_league_details
Get detailed information about a league
get_league_table
Get league standings/table
get_next_events
Get upcoming events for a team
get_player_details
Get detailed information about a player
get_team_details
Get detailed information about a team
list_all_countries
List all countries with sports data
list_all_leagues
List all available leagues
list_all_sports
List all available sports
search_events
Search for sports events by name
search_players
Returns player metadata including team and nationality. Search for players by name
search_teams
Returns team metadata including ID, sport, league, and country. Search for sports teams by name
Example Prompts for SportsDB in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with SportsDB immediately.
"Show me the current Premier League standings."
Troubleshooting SportsDB MCP Server with Pydantic AI
Common issues when connecting SportsDB to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiSportsDB + Pydantic AI FAQ
Common questions about integrating SportsDB 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 SportsDB 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 SportsDB to Pydantic AI
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.