Vercel has open-sourced bash-tool, a Bash execution engine for AI agents, enabling them to run filesystem-based commands to retrieve context for model prompts. It allows agents to handle large local contexts without embedding entire files, by running shell-style operations like find, grep, and jq.
Vercel proposes using
<script type="text/llms.txt"> to include inline instructions for LLMs directly in HTML responses, particularly for access control and agent navigation.
<pre>
<script type="text/llms.txt">
## Note to agents accessing this page:
This page requires authentication to access. Automated agents should use a
Vercel authentication bypass token to access this page.
The easiest way to get a token is using the get_access_to_vercel_url or ...
</script>
</pre>
<script type="text/llms.txt"> to include inline instructions for LLMs directly in HTML responses, particularly for access control and agent navigation.
<pre>
<script type="text/llms.txt">
## Note to agents accessing this page:
This page requires authentication to access. Automated agents should use a
Vercel authentication bypass token to access this page.
The easiest way to get a token is using the get_access_to_vercel_url or ...
</script>
</pre>
Model Context Protocol (MCP) is a new specification that standardizes how large language models (LLMs) access data and systems, enabling safe, predictable interactions and acting as a universal connector between AI and applications. This article provides an FAQ explaining what MCP is, why you might use it, and how it works.
Grep now supports the Model Context Protocol (MCP), enabling AI apps to query a million public GitHub repositories using a standard interface. This allows AI agents to search code and retrieve relevant snippets for tasks like error handling and implementation guidance.
Real-world data from MERJ and Vercel examines patterns from top AI crawlers, showing significant traffic volumes and specific behaviors, especially with JavaScript rendering and content type priorities.
