Vercel AI SDK Tool Patterns

Vercel AI SDK Tool Patterns

Package-specific patterns for @youdotcom-oss/​ai-sdk-plugin. Use these patterns when creating AI SDK tools.

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Tool Function Structure

Each tool function follows this pattern:

export const youToolName = (config: YouToolsConfig = {}) => {
  const apiKey = config.apiKey ?? process.env.YDC_API_KEY;

  return tool({
    description: 'Tool description for AI model',
    inputSchema: ZodSchema,
    execute: async (params) => {
      if (!apiKey) {
        throw new Error('YDC_API_KEY is required');
      }

      const response = await callApiUtility({
        params,
        YDC_API_KEY: apiKey,
        getUserAgent,
      });

      // Return raw API response for maximum flexibility
      return response;
    },
  });
};

Key Components

• config - Optional configuration with API key
• tool() - AI SDK tool wrapper
• inputSchema - Zod schema that enables AI to construct intelligent queries
• execute() - Async function that calls You.com API
• Returns raw API response for maximum flexibility

Input Schemas Enable Smart Queries

The most critical pattern is providing comprehensive input schemas. These schemas allow AI agents to construct intelligent, well-formed queries with all available parameters.

Always use schemas from @youdotcom-oss/​mcp

// ✅ Import from @youdotcom-oss/​mcp
import { SearchQuerySchema } from '@youdotcom-oss/​mcp';

export const youSearch = (config: YouToolsConfig = {}) => {
  return tool({
    description: '...',
    inputSchema: SearchQuerySchema,  // Enables AI to use all search parameters
    execute: async (params) => { ... },
  });
};

// ❌ Don't duplicate or simplify schemas
const MySearchSchema = z.object({ query: z.string() });  // Missing filters!
inputSchema: MySearchSchema

Why this matters:

• Rich schemas enable AI to use advanced query parameters (filters, freshness, country, etc.)
• AI can construct more intelligent queries based on user intent
• Prevents duplicating schema definitions across packages
• Ensures consistency with MCP server schemas

Schema Examples

SearchQuerySchema - Enables AI to use search filters:

// AI can automatically add filters based on user intent:
// "Latest news about AI" → { query: "AI", freshness: "day" }
// "Python tutorials" → { query: "Python tutorials" }

ExpressAgentInputSchema - Enables AI to control agent behavior:

// AI can enable/​disable web search based on query type:
// "What is 2+2?" → { input: "What is 2+2?", tools: [] }  // No web needed
// "Latest GDP data" → { input: "Latest GDP data", tools: ["search"] }

ContentsQuerySchema - Enables AI to extract from multiple URLs:

// AI can extract from multiple pages and choose format:
// { urls: ["url1", "url2"], format: "markdown" }

API Key Handling

Always provide environment variable fallback

// ✅ Automatic environment variable fallback
const apiKey = config.apiKey ?? process.env.YDC_API_KEY;

// Usage - no explicit apiKey needed if YDC_API_KEY is set
const search = youSearch();

// Or override with explicit config
const search = youSearch({ apiKey: 'custom-key' });

Always validate API key before API calls

// ✅ Check API key in execute function
execute: async (params) => {
  if (!apiKey) {
    throw new Error('YDC_API_KEY is required');
  }
  const response = await callApi(...);
}

// ❌ Don't skip validation
execute: async (params) => {
  const response = await callApi(...); // May fail with unclear error
}

Why this pattern?

• Provides clear error message when API key is missing
• Fails fast before making API calls
• Helps users identify configuration issues quickly

Response Format

Always return raw API response

// ✅ Return raw API response
execute: async (params) => {
  const response = await fetchSearchResults({
    searchQuery: params,
    YDC_API_KEY: apiKey,
    getUserAgent,
  });

  return response;  // Raw response for maximum flexibility
}

// ❌ Don't format or transform responses
return {
  text: formatResponse(response),
  data: response,
};

Why raw responses?

• Maximum flexibility for AI SDK to process results
• No information loss from formatting
• AI SDK handles presentation layer
• Easier to debug (see actual API response)

Tool Descriptions

Write descriptions that guide AI behavior and explain when to use the tool:

// ✅ Clear guidance for AI model
description: 'Search the web for current information, news, articles, and content using You.com. Returns web results with snippets and news articles. Use this when you need up-to-date information or facts from the internet.'

// ✅ Explains tool purpose and use case
description: 'Fast AI agent powered by You.com that provides quick answers with optional web search. Use this for straightforward queries that benefit from real-time web information.'

// ❌ Too brief
description: 'Search the web'

// ❌ For humans instead of AI
description: 'This tool allows you to search'

Best Practices

1. Use comprehensive input schemas - Enable AI to construct intelligent queries with all available parameters
2. Always use schemas from @youdotcom-oss/​mcp - Don't duplicate, import from shared package
3. Return raw API responses - Maximum flexibility, no information loss
4. Validate API keys - Fail fast with clear error messages
5. Use environment variable fallback - Automatic YDC_API_KEY detection
6. Write clear tool descriptions - Guide AI on when and how to use tools

Related Resources

• Vercel AI SDK Documentation: https://sdk.vercel.ai/docs
• Package AGENTS.md: packages/​ai-sdk-plugin/​AGENTS.md
• MCP Package (schemas): packages/​mcp/
• API Documentation: packages/​ai-sdk-plugin/​docs/​API.md