I built an IOS markdown editor that uses MCP to sync artifacts to your phone. This allows you to read and edit artifacts from and for your agent on the go.
The artifacts can be markdown or html. Your AI agent can even read back in your edits or changes.
I still need to improve the MCP connection flow, but there is complexity there that is hard to overcome.
A CLI is a wrapper for an API. It is opinionated on how it should work but fundamentally provides the scope and auth context to the Agent. In this way it is bound to a server by needing to be installed.
An API can be operated by an agent, or chat bot, if it gets API scope, Auth Shape and context. It has the benefit of operating across agents and servers by not requiring any install.
I have found agents very adept at going directly to the API if given those 3 things.
How do you approach?
The best use of MCP is to make an introduction not to carry the conversation.
Any agent or chat bot understands remote connections, auth structure and APIs. For it to build a specific connection you need to provide the AI with:
- endpoint and API documentation
- Auth structure
- Context
With these things almost any agent or chatbot can connect and operate a remote service.
Thus when you structure an MCP to provide these three things to an agent you enable the agent to operate the API directly; operating outside the MCP connection, greatly reducing token usage and context bloat.
You get MCP for discovery and context delivery. But it is not in the path of the operation and does not require CLI installation, eliminating much configuration pain.
Auth is a consideration. In my implementation I provide a pointer to the Auth file(env), service or key name, but the MCP never holds those sensitive credentials.
At usejoshua.com you can play with it or see the MCP data structures.