Consistent Web Search and Fetch Across Every Model

David Bai discusses the introduction of two new tools, openrouter:web_search and openrouter:web_fetch, which enable consistent web search and content retrieval across different models without requiring client-side implementation. These tools standardize the way models handle web search and content fetching, allowing for consistent behavior regardless of the model or provider being used, such as GPT-5.5, Claude, or Kimi. The web search tool supports various engines, each with different pricing and capabilities, such as domain filtering and configurable result contexts, while the web fetch tool allows for full-page content retrieval with options to control content size and domain access. These innovations aim to replace the previous reliance on web search plugins, which limited the model's ability to define search parameters and frequency, although support for these tools requires models capable of tool-calling. The tools provide a unified schema for invoking and parsing search and fetch results, ensuring predictable and consistent outcomes across different AI models.

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