How to Use the The Odds API MCP in LlamaIndex

Q: How does LlamaIndex ingest The Odds API data for RAG?

You call the MCP tool (like getodds) and then pass that output to your indexing pipeline. The resulting structured JSON is stored in a vector store, making it searchable by meaning, not just keyword.

Q: Can I query past odds using LlamaIndex and The Odds API?

Yes. By running getscores or other data through the indexer, you ground your knowledge base with historical results. This lets you ask questions about past performance that aren't obvious.

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Setup The Odds API with LlamaIndex

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Indexing Odds Data for Semantic Search

When you run `get_odds`, the output becomes a chunk of searchable data in your vector store. Instead of just getting a result, you index it so later queries can find specific odds ratios or market discrepancies months from now.

Combining Live Scores with LlamaIndex

`get_scores` output feeds directly into your knowledge base. You can query past sessions and ask questions like, 'What were the scores for this matchup last season?' Your RAG application answers using the actual data retrieved.

Structuring Knowledge with MCP Server Tools

`list_sports` helps you build a structured index of available markets. You can combine that list with other documents, making your whole knowledge base hyper-specific to sports analytics and betting categories.

Setup guide

Set up The Odds API MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all The Odds API MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to The Odds API tools.",
)
response = await agent.run("List recent The Odds API data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by The Odds API. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about The Odds API MCP in LlamaIndex

You call the MCP tool (like `get_odds`) and then pass that output to your indexing pipeline. The resulting structured JSON is stored in a vector store, making it searchable by meaning, not just keyword.

Yes. By running `get_scores` or other data through the indexer, you ground your knowledge base with historical results. This lets you ask questions about past performance that aren't obvious.

It treats the MCP tools like any other data source. You pass `get_odds`, `get_scores`, and `list_sports` to your agent, which then uses them to build a unified index.

It's designed for programmatic access. Since the output is clean, structured data (odds/scores), it integrates perfectly into RAG systems without needing complex cleanup.

This server primarily deals with odds and scores data. The index stores these quantitative betting statistics, ensuring no personal identifying information is included in the vector store.

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