How to Use the The Odds API MCP in Pydantic AI

Q: Can I use The Odds API for multiple sports simultaneously?

You can list all available options using listsports and then process them through your agent. Since the outputs are type-checked, you'll get a predictable structure regardless of how many tools calls you make.

Guarantee data integrity when fetching betting information with Pydantic AI's type checking.

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Connect The Odds API MCP to Pydantic AI

Create your Vinkius account to connect The Odds API to Pydantic AI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup The Odds API with Pydantic AI

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Fetch odds for a specific sport.

The `get_odds` tool returns structured, validated data that your agent processes using Pydantic models. This means you never have to worry about the API returning an unexpected field name. The agent fails loudly with a validation error if The Odds API sends anything that doesn't match your defined schema, eliminating silent corruption from bad responses.

Retrieve live or past scores.

Use `get_scores` to check game status. Because every response is validated against Pydantic models at runtime, you get guaranteed types for the score, date, and status. It’s a huge win for correctness: your agent knows exactly what structure to expect, so it can build reliable production pipelines.

List all available sports.

`list_sports` provides the necessary list of identifiers. The data returned is immediately validated by Pydantic models, giving you a guaranteed clean array of sport objects. This type safety means your agent can iterate over and trust every single item it receives from the toolset.

Setup guide

Set up The Odds API MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "the-odds-api-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to The Odds API tools.",
)

result = await agent.run("List recent The Odds API transactions")
print(result.output)

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Common questions about The Odds API MCP in Pydantic AI

The MCP Server provides all tools, but Pydantic AI adds a critical layer: runtime validation. Every piece of data returned by `get_odds` or `get_scores` must match the model you defined; otherwise, the agent throws an explicit error.

That's where Pydantic shines. If the underlying API structure shifts and sends data that doesn't match your model, the agent won't hallucinate or proceed incorrectly; it will fail with a clear validation error.

Yes. For mission-critical applications where data correctness is more important than speed, the strong type checking provided by Pydantic models makes this server ideal. You get guaranteed schema adherence.

You can list all available options using `list_sports` and then process them through your agent. Since the outputs are type-checked, you'll get a predictable structure regardless of how many tools calls you make.

The server handles betting odds, scores, and sports metadata. It deals exclusively in public market information; it has zero access to private user credentials or financial records.

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