In this article, we will look at this progression that has happened from basic tool use to function calling to the Model Context Protocol, allowing the LLMs to go from isolated text generation tools to assistants that can do interesting stuff for the end users.
MCP collapses the integration count, but it inherits the plugin trust problem: MCP servers are third-party tools whose self-descriptions become the model's menu, so vetting lands on whoever edits the host's server list. Plugins failed partly on exactly that.
The deeper lesson is that AI search only creates business value when it is tied to operational truth. Grocery is messy: products go out of stock, stores vary by location, names are inconsistent, and customer language rarely matches catalog taxonomy. A standalone “smart search” layer is not enough if it is disconnected from fresh inventory and business rules.
MCP collapses the integration count, but it inherits the plugin trust problem: MCP servers are third-party tools whose self-descriptions become the model's menu, so vetting lands on whoever edits the host's server list. Plugins failed partly on exactly that.
The deeper lesson is that AI search only creates business value when it is tied to operational truth. Grocery is messy: products go out of stock, stores vary by location, names are inconsistent, and customer language rarely matches catalog taxonomy. A standalone “smart search” layer is not enough if it is disconnected from fresh inventory and business rules.
MCP is the hidden gem of a powerful AI system.
Curate your systems with right MCP connections and monitor its usage carefully.
Be cautious of enabling any production access to you agents as it can backfire like the Pocket OS disaster.