Comment by theturtletalks
13 hours ago
Easy. Just tell the LLM to use the Linear CLI or hit their API directly. I’m only half-joking. Older models were terrible at doing that reliably, which is exactly why we created MCP.
Our SaaS has a built-in AI assistant that only performs actions for the user through our GraphQL API. We wrapped the API in simple MCP tools that give the model clean introspection and let us inject the user’s authenticated session cookie directly. The LLM never deals with login, tokens, or permissions. It can just act with the full rights of the logged-in user.
MCP still has value today, especially with models that can easily call tools but can’t stick to prompt. From what I’ve seen in Claude’s roadmap, the future may shift toward loading “skills” that describe exactly how to call a GraphQL API (in my case), then letting the model write the code itself. That sounds good on paper, but an LLM generating and running API code on the fly is less consistent and more error-prone than calling pre-built tools.
Easy if you ignore the security aspects. You want to hand over your tokens to your LLM so it can script up a tool that can access it? The value I see in MCP is that you can give an LLM access to services via socket without giving it access to the tokens/credentials required to access said service. It provides at least one level of security that way.
The point of the example seemed to be connecting easily to a scoped GraphQL API.