How to Give an LLM Web Browsing with Function Calling: OpenAI, Anthropic and MCP Tool Definitions

Efficient autonomous LLM web workflows depend on converting verbose web content into compact, useful tool outputs, since raw HTML can exceed typical context windows while main-content Markdown and query-focused highlights substantially reduce token use. The guide presents Context.dev as a unified API for live web search, page scraping, passage highlights, and schema-based JSON extraction, integrated through OpenAI and Anthropic function calling or Model Context Protocol servers. It recommends separating URL discovery, page reading, and typed-data extraction; using strict tool schemas; removing boilerplate server-side; limiting returned characters; and preserving source URLs for citations. It also describes production safeguards such as partial-result timeouts, checking output-level success flags even after HTTP 200 responses, and passing structured errors such as blocked sites, dead links, image-only PDFs, or oversized content back to the model so it can choose alternatives. For tasks requiring specific facts rather than narrative interpretation, the guide advises structured JSON extraction with nullable fields and explicit instructions to rely only on page-stated information.

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