The Odds API MCP Server for Pydantic AI 3 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect The Odds API 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 The Odds API "
"(3 tools)."
),
)
result = await agent.run(
"What tools are available in The Odds API?"
)
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 The Odds API MCP Server
Equip your AI agent with real-time sports market intelligence via The Odds API MCP server. This integration provides instant access to live odds from dozens of bookmakers across major sports leagues including NFL, NBA, MLB, EPL, and more. Your agent can list all supported sports, retrieve current market odds for specific regions, and check live or historical scores. Whether you are analyzing market trends, tracking your favorite teams, or building a betting advisor, your agent acts as a dedicated sports data analyst through natural conversation.
Pydantic AI validates every The Odds API tool response against typed schemas, catching data inconsistencies at build time. Connect 3 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
- Live Odds Retrieval — Get real-time odds for upcoming games from multiple bookmakers.
- Sports Discovery — List all available sports and leagues supported by the API.
- Score Tracking — Check live and recently completed game scores for various sports.
- Market Comparison — Compare prices across different bookmakers and regions (US, UK, EU, AU).
- Historical Auditing — Summarize and analyze past game results and market movements.
The The Odds API MCP Server exposes 3 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 The Odds API to Pydantic AI via MCP
Follow these steps to integrate the The Odds API 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 3 tools from The Odds API with type-safe schemas
Why Use Pydantic AI with the The Odds API MCP Server
Pydantic AI provides unique advantages when paired with The Odds API 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 The Odds API integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your The Odds API connection logic from agent behavior for testable, maintainable code
The Odds API + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the The Odds API MCP Server delivers measurable value.
Type-safe data pipelines: query The Odds API with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple The Odds API tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query The Odds API and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock The Odds API responses and write comprehensive agent tests
The Odds API MCP Tools for Pydantic AI (3)
These 3 tools become available when you connect The Odds API to Pydantic AI via MCP:
get_odds
Get odds for a specific sport
get_scores
Get live and past scores
list_sports
List all available sports
Example Prompts for The Odds API in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with The Odds API immediately.
"Show me the current odds for the next NFL games."
"Check the live scores for the English Premier League."
"List all sports available on The Odds API."
Troubleshooting The Odds API MCP Server with Pydantic AI
Common issues when connecting The Odds API to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiThe Odds API + Pydantic AI FAQ
Common questions about integrating The Odds API 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 The Odds API 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 The Odds API to Pydantic AI
Get your token, paste the configuration, and start using 3 tools in under 2 minutes. No API key management needed.