Web Access
Overview
Web Access is a unified tool that enables AI models to access up-to-date information from the internet in two ways:
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URL Fetching – Retrieves the full content of a specific web page, API documentation, or GitHub repository, allowing the AI to work with the exact data provided. For more information, check URL fetching page.
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Web Search – Finds and retrieves relevant, recent information from the web when a specific source is not provided.
By combining these two capabilities, Web Access allows the AI to either directly access known resources or search for the most relevant sources available online.
Key Capabilities
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Access to Real-Time Information – Provides the AI with current data beyond its training cut-off date.
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Precise Content Retrieval – Fetches exact content from a provided URL, including HTML, API endpoints, or GitHub repository files.
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Contextual Web Searching – Finds relevant public information when a user query requires discovery rather than a direct URL.
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Private GitHub Access – With a GitHub Personal Access Token (PAT), the AI can retrieve data from private repositories. Users should revoke the token after use for security purposes.
Best Practices and Limitations
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Use public URLs where possible – Content behind logins or restricted by region may not be retrievable without credentials.
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Consider site restrictions – Some websites block automated retrieval or limit requests.
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Be cautious with dynamic pages – Pages heavily reliant on JavaScript may not fully load during retrieval.
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Use minimum required permissions for GitHub PATs – For private repositories, grant only necessary scopes and revoke access after use.
Summary
Web Access combines direct URL retrieval with intelligent web search to provide AI models with both targeted and broad access to online information. Whether you need to parse data from a known source or gather recent context from multiple sources, Web Access offers a streamlined and flexible approach.
Last Updated: aug 2025