AI Pulse · Under the Radar

The useful part of the latest MCP discussion is that it moves past demos. Practitioners are describing where the Model Context Protocol is already being used in real work. MCP is the standard that lets an AI assistant connect to tools and systems, such as error tracking, internal data, or workflow services.

Examples in the thread include Adidas using both local stdio and remote MCP setups for autonomous workflows, an endurance-sports analytics SaaS that says it has run MCP since March, and teams connecting Claude to Sentry to produce automated error digests. These are not broad claims that every company should rush to copy. They are practical signals that the protocol is finding its way into operating workflows.

The more valuable details are the problems people hit: CORS configuration issues, missing OAuth Dynamic Client Registration, and too much tool context being passed to the model. That last one matters. Give an AI too many tools, descriptions, and options, and it can become slower, less reliable, and harder to govern.

For business owners, the lesson is simple. The question is no longer whether AI can talk to your systems. It can. The work is deciding which connections are useful, what permissions they need, and where human checks still belong. This is the kind of thing we build into an AI command centre, connecting useful tools without turning every internal system into an uncontrolled AI experiment.