I’ve deployed mcp to prod on numerous internal apps where ai-integration has been first party requirement. Obviously, agents can use an existing api, however maximum capability requires it to be well designed and documented. As many of you may have experienced, that is not something that can be assumed and improvements may be “impossible” or at least out of current scope.
That’s where mcp (and recently webmcp) have their benefits. By design, the protocols provide predictable structure that allows agents to discover and use the tools you provide.
I’ve had success using mcp variously as an agent friendly wrapper to an existing api and as a greenfield product designed for agentic workflows. Agentic workflows can string together multiple api endpoints together into a single tool call and or use their own dedicated backends.
To share my learnings, I’ve released a free tool at https://anc.dev . While It’s still very much a work in progress, I’d love dialog and constructive feedback.
That’s where mcp (and recently webmcp) have their benefits. By design, the protocols provide predictable structure that allows agents to discover and use the tools you provide.
I’ve had success using mcp variously as an agent friendly wrapper to an existing api and as a greenfield product designed for agentic workflows. Agentic workflows can string together multiple api endpoints together into a single tool call and or use their own dedicated backends.
To share my learnings, I’ve released a free tool at https://anc.dev . While It’s still very much a work in progress, I’d love dialog and constructive feedback.