# How to integrate The odds api MCP with Vercel AI SDK v6 ```json { "title": "How to integrate The odds api MCP with Vercel AI SDK v6", "toolkit": "The odds api", "toolkit_slug": "the_odds_api", "framework": "Vercel AI SDK", "framework_slug": "ai-sdk", "url": "https://composio.dev/toolkits/the_odds_api/framework/ai-sdk", "markdown_url": "https://composio.dev/toolkits/the_odds_api/framework/ai-sdk.md", "updated_at": "2026-05-12T10:28:23.247Z" } ``` ## Introduction This guide walks you through connecting The odds api to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working The odds api agent that can show live nba game odds right now, list upcoming soccer matches this weekend, get current scores for mlb games through natural language commands. This guide will help you understand how to give your Vercel AI SDK agent real control over a The odds api account through Composio's The odds api MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate The odds api with - [OpenAI Agents SDK](https://composio.dev/toolkits/the_odds_api/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/the_odds_api/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/the_odds_api/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/the_odds_api/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/the_odds_api/framework/codex) - [OpenClaw](https://composio.dev/toolkits/the_odds_api/framework/openclaw) - [Hermes](https://composio.dev/toolkits/the_odds_api/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/the_odds_api/framework/cli) - [Google ADK](https://composio.dev/toolkits/the_odds_api/framework/google-adk) - [LangChain](https://composio.dev/toolkits/the_odds_api/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/the_odds_api/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/the_odds_api/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/the_odds_api/framework/crew-ai) ## TL;DR Here's what you'll learn: - How to set up and configure a Vercel AI SDK agent with The odds api integration - Using Composio's Tool Router to dynamically load and access The odds api tools - Creating an MCP client connection using HTTP transport - Building an interactive CLI chat interface with conversation history management - Handling tool calls and results within the Vercel AI SDK framework ## What is Vercel AI SDK? The Vercel AI SDK is a TypeScript library for building AI-powered applications. It provides tools for creating agents that can use external services and maintain conversation state. Key features include: - streamText: Core function for streaming responses with real-time tool support - MCP Client: Built-in support for Model Context Protocol via @ai-sdk/mcp - Step Counting: Control multi-step tool execution with stopWhen: stepCountIs() - OpenAI Provider: Native integration with OpenAI models ## What is the The odds api MCP server, and what's possible with it? The The odds api MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your The odds api account. It provides structured and secure access to real-time sports betting odds and event data, so your agent can look up live odds, fetch sports schedules, track scores, and analyze betting markets worldwide on your behalf. - Live odds retrieval for events: Instantly fetch up-to-date betting odds for specific games or matches across multiple bookmakers and markets. - Comprehensive sports and event discovery: Ask your agent to list current and upcoming sporting events, including which sports are in season and event-specific details. - Participant and team lookups: Easily get information about teams, players, or participants involved in any listed sport to inform your analysis or predictions. - Live and recent score tracking: Stay up to date with real-time scores and recently completed game results for your favorite sports. - Regional and market-based odds filtering: Compare odds for specific regions, bookmakers, or betting markets to find the best opportunities. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `THE_ODDS_API_GET_EVENT_MARKETS` | Get Event Markets | Tool to retrieve available market keys for each bookmaker for a specific event. Returns only recently seen markets - not a comprehensive list. More markets become available as the event's commence time approaches. | | `THE_ODDS_API_GET_EVENT_ODDS` | Get Event Odds | Tool to retrieve odds for a specific event. Use after confirming sport key via THE_ODDS_API_GET_SPORTS and event ID via THE_ODDS_API_GET_EVENTS; mismatched values return empty payloads rather than errors. | | `THE_ODDS_API_GET_EVENTS` | Get Events | Tool to fetch live and upcoming events for a specified sport. Use when you need event listings including odds. All timestamps are UTC. Avoid high-frequency polling; batch requests and implement backoff to prevent throttling. | | `THE_ODDS_API_GET_ODDS` | Get Odds | Tool to fetch live and upcoming event odds for a specified sport, including bookmakers, regions, and markets. Use after retrieving sports via GET_SPORTS; filter by region, market, or event IDs. Response is nested bookmakers → markets → outcomes; not all bookmakers expose every market for every event, so handle missing keys and empty arrays defensively. Combining multiple regions, markets, and eventIds produces large payloads — narrow to one region or specific eventIds where possible. | | `THE_ODDS_API_GET_PARTICIPANTS` | Get Participants | Tool to fetch list of participants (teams or players) for a specified sport. Use after confirming you have a valid sport key. | | `THE_ODDS_API_GET_SCORES` | Get Scores | Tool to return live and recently completed event scores for a sport. Use after selecting a sport key to inspect current and recent game scores. Missing results may indicate unsupported competitions, not absent events. When identifying specific fixtures, match by both team names and date to avoid confusion with multi-leg ties or similarly named teams. | | `THE_ODDS_API_GET_SPORTS` | Get Sports | Tool to retrieve a list of in-season sports. Use when you need sports data; set 'all' to true to include out-of-season sports. Sport keys returned here must be passed to downstream tools like THE_ODDS_API_GET_EVENTS and THE_ODDS_API_GET_EVENT_ODDS — mismatched or guessed keys return empty payloads. Similarly named leagues and qualifier competitions appear as distinct entries with unique keys; verify the exact key before use. | | `THE_ODDS_API_GET_V3_ODDS` | Get Odds (V3 Legacy) | Tool to fetch odds using the legacy V3 API endpoint. Returns upcoming and live games with odds for a given sport, region, and market. Use for legacy integrations; V4 API is recommended for new implementations. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The The odds api MCP server is an implementation of the Model Context Protocol that connects your AI agent to The odds api. It provides structured and secure access so your agent can perform The odds api operations on your behalf through a secure, permission-based interface. With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster. ## Step-by-step Guide ### 1. Prerequisites Before you begin, make sure you have: - Node.js and npm installed - A Composio account with API key - An OpenAI API key ### 1. Getting API Keys for OpenAI and Composio OpenAI API Key - Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) and create an API key. You'll need credits to use the models, or you can connect to another model provider. - Keep the API key safe. Composio API Key - Log in to the [Composio dashboard](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs). - Navigate to your API settings and generate a new API key. - Store this key securely as you'll need it for authentication. ### 2. Install required dependencies First, install the necessary packages for your project. What you're installing: - @ai-sdk/openai: Vercel AI SDK's OpenAI provider - @ai-sdk/mcp: MCP client for Vercel AI SDK - @composio/core: Composio SDK for tool integration - ai: Core Vercel AI SDK - dotenv: Environment variable management ```bash npm install @ai-sdk/openai @ai-sdk/mcp @composio/core ai dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's needed: - OPENAI_API_KEY: Your OpenAI API key for GPT model access - COMPOSIO_API_KEY: Your Composio API key for tool access - COMPOSIO_USER_ID: A unique identifier for the user session ```bash OPENAI_API_KEY=your_openai_api_key_here COMPOSIO_API_KEY=your_composio_api_key_here COMPOSIO_USER_ID=your_user_id_here ``` ### 4. Import required modules and validate environment What's happening: - We're importing all necessary libraries including Vercel AI SDK's OpenAI provider and Composio - The dotenv/config import automatically loads environment variables - The MCP client import enables connection to Composio's tool server ```typescript import "dotenv/config"; import { openai } from "@ai-sdk/openai"; import { Composio } from "@composio/core"; import * as readline from "readline"; import { streamText, type ModelMessage, stepCountIs } from "ai"; import { createMCPClient } from "@ai-sdk/mcp"; const composioAPIKey = process.env.COMPOSIO_API_KEY; const composioUserID = process.env.COMPOSIO_USER_ID; if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set"); if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set"); if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set"); const composio = new Composio({ apiKey: composioAPIKey, }); ``` ### 5. Create Tool Router session and initialize MCP client What's happening: - We're creating a Tool Router session that gives your agent access to The odds api tools - The create method takes the user ID and specifies which toolkits should be available - The returned mcp object contains the URL and authentication headers needed to connect to the MCP server - This session provides access to all The odds api-related tools through the MCP protocol ```typescript async function main() { // Create a tool router session for the user const session = await composio.create(composioUserID!, { toolkits: ["the_odds_api"], }); const mcpUrl = session.mcp.url; ``` ### 6. Connect to MCP server and retrieve tools What's happening: - We're creating an MCP client that connects to our Composio Tool Router session via HTTP - The mcp.url provides the endpoint, and mcp.headers contains authentication credentials - The type: "http" is important - Composio requires HTTP transport - tools() retrieves all available The odds api tools that the agent can use ```typescript const mcpClient = await createMCPClient({ transport: { type: "http", url: mcpUrl, headers: session.mcp.headers, // Authentication headers for the Composio MCP server }, }); const tools = await mcpClient.tools(); ``` ### 7. Initialize conversation and CLI interface What's happening: - We initialize an empty messages array to maintain conversation history - A readline interface is created to accept user input from the command line - Instructions are displayed to guide the user on how to interact with the agent ```typescript let messages: ModelMessage[] = []; console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n"); console.log( "Ask any questions related to the_odds_api, like summarize my last 5 emails, send an email, etc... :)))\n", ); const rl = readline.createInterface({ input: process.stdin, output: process.stdout, prompt: "> ", }); rl.prompt(); ``` ### 8. Handle user input and stream responses with real-time tool feedback What's happening: - We use streamText instead of generateText to stream responses in real-time - toolChoice: "auto" allows the model to decide when to use The odds api tools - stopWhen: stepCountIs(10) allows up to 10 steps for complex multi-tool operations - onStepFinish callback displays which tools are being used in real-time - We iterate through the text stream to create a typewriter effect as the agent responds - The complete response is added to conversation history to maintain context - Errors are caught and displayed with helpful retry suggestions ```typescript rl.on("line", async (userInput: string) => { const trimmedInput = userInput.trim(); if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) { console.log("\nGoodbye!"); rl.close(); process.exit(0); } if (!trimmedInput) { rl.prompt(); return; } messages.push({ role: "user", content: trimmedInput }); console.log("\nAgent is thinking...\n"); try { const stream = streamText({ model: openai("gpt-5"), messages, tools, toolChoice: "auto", stopWhen: stepCountIs(10), onStepFinish: (step) => { for (const toolCall of step.toolCalls) { console.log(`[Using tool: ${toolCall.toolName}]`); } if (step.toolCalls.length > 0) { console.log(""); // Add space after tool calls } }, }); for await (const chunk of stream.textStream) { process.stdout.write(chunk); } console.log("\n\n---\n"); // Get final result for message history const response = await stream.response; if (response?.messages?.length) { messages.push(...response.messages); } } catch (error) { console.error("\nAn error occurred while talking to the agent:"); console.error(error); console.log( "\nYou can try again or restart the app if it keeps happening.\n", ); } finally { rl.prompt(); } }); rl.on("close", async () => { await mcpClient.close(); console.log("\n👋 Session ended."); process.exit(0); }); } main().catch((err) => { console.error("Fatal error:", err); process.exit(1); }); ``` ## Complete Code ```typescript import "dotenv/config"; import { openai } from "@ai-sdk/openai"; import { Composio } from "@composio/core"; import * as readline from "readline"; import { streamText, type ModelMessage, stepCountIs } from "ai"; import { createMCPClient } from "@ai-sdk/mcp"; const composioAPIKey = process.env.COMPOSIO_API_KEY; const composioUserID = process.env.COMPOSIO_USER_ID; if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set"); if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set"); if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set"); const composio = new Composio({ apiKey: composioAPIKey, }); async function main() { // Create a tool router session for the user const session = await composio.create(composioUserID!, { toolkits: ["the_odds_api"], }); const mcpUrl = session.mcp.url; const mcpClient = await createMCPClient({ transport: { type: "http", url: mcpUrl, headers: session.mcp.headers, // Authentication headers for the Composio MCP server }, }); const tools = await mcpClient.tools(); let messages: ModelMessage[] = []; console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n"); console.log( "Ask any questions related to the_odds_api, like summarize my last 5 emails, send an email, etc... :)))\n", ); const rl = readline.createInterface({ input: process.stdin, output: process.stdout, prompt: "> ", }); rl.prompt(); rl.on("line", async (userInput: string) => { const trimmedInput = userInput.trim(); if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) { console.log("\nGoodbye!"); rl.close(); process.exit(0); } if (!trimmedInput) { rl.prompt(); return; } messages.push({ role: "user", content: trimmedInput }); console.log("\nAgent is thinking...\n"); try { const stream = streamText({ model: openai("gpt-5"), messages, tools, toolChoice: "auto", stopWhen: stepCountIs(10), onStepFinish: (step) => { for (const toolCall of step.toolCalls) { console.log(`[Using tool: ${toolCall.toolName}]`); } if (step.toolCalls.length > 0) { console.log(""); // Add space after tool calls } }, }); for await (const chunk of stream.textStream) { process.stdout.write(chunk); } console.log("\n\n---\n"); // Get final result for message history const response = await stream.response; if (response?.messages?.length) { messages.push(...response.messages); } } catch (error) { console.error("\nAn error occurred while talking to the agent:"); console.error(error); console.log( "\nYou can try again or restart the app if it keeps happening.\n", ); } finally { rl.prompt(); } }); rl.on("close", async () => { await mcpClient.close(); console.log("\n👋 Session ended."); process.exit(0); }); } main().catch((err) => { console.error("Fatal error:", err); process.exit(1); }); ``` ## Conclusion You've successfully built a The odds api agent using the Vercel AI SDK with streaming capabilities! This implementation provides a powerful foundation for building AI applications with natural language interfaces and real-time feedback. Key features of this implementation: - Real-time streaming responses for a better user experience with typewriter effect - Live tool execution feedback showing which tools are being used as the agent works - Dynamic tool loading through Composio's Tool Router with secure authentication - Multi-step tool execution with configurable step limits (up to 10 steps) - Comprehensive error handling for robust agent execution - Conversation history maintenance for context-aware responses You can extend this further by adding custom error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows. ## How to build The odds api MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/the_odds_api/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/the_odds_api/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/the_odds_api/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/the_odds_api/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/the_odds_api/framework/codex) - [OpenClaw](https://composio.dev/toolkits/the_odds_api/framework/openclaw) - [Hermes](https://composio.dev/toolkits/the_odds_api/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/the_odds_api/framework/cli) - [Google ADK](https://composio.dev/toolkits/the_odds_api/framework/google-adk) - [LangChain](https://composio.dev/toolkits/the_odds_api/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/the_odds_api/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/the_odds_api/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/the_odds_api/framework/crew-ai) ## Related Toolkits - [Excel](https://composio.dev/toolkits/excel) - Microsoft Excel is a robust spreadsheet application for organizing, analyzing, and visualizing data. 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It helps streamline how you gather, organize, and analyze responses. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and The odds api MCP? With a standalone The odds api MCP server, the agents and LLMs can only access a fixed set of The odds api tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from The odds api and many other apps based on the task at hand, all through a single MCP endpoint. ### Can I use Tool Router MCP with Vercel AI SDK v6? Yes, you can. Vercel AI SDK v6 fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right The odds api tools. ### Can I manage the permissions and scopes for The odds api while using Tool Router? Yes, absolutely. You can configure which The odds api scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do. ### How safe is my data with Composio Tool Router? All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your The odds api data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)