gmaysI still prefer MCP over skills
I could not agree any less with the author. I don’t want APIs, I want agents to use the same CLI tooling I already use that is locally available. If my agents are using CLI tooling anyways there is no need to add an extra layer via MCP.
I don’t want remote MCP calls, I don’t even want remote models but that’s cost prohibitive.
If I need to call an API, a skill with existing CLI tooling is more than capable.
Ok, but there are still many environments where an LLM will not have access to a CLI. In those situations, skills calling CLI tools to hook into APIs are DOA.
idk, just have a standard internet request tool that skills can describe endpoints to. like you could mock `curl` even for the same CLI feel
What are the advantages of using an environment that doesn't have access to a CLI, only having to run/maintain your own server, or pay someone else to maintain that server, so AI has access to tools? Can't you just use AI in the said server?
Obvious example is a corporate chatbot (if it's using tools, probably for internal use). Non-technical users might be accessing it from a phone or locked-down corporate device, and you probably don't want to run a CLI in a sandbox somewhere for every session, so you'd like the LLM to interface with some kind of API instead.
Although, I think MCP is not really appropriate for this either. (And frankly I don't think chatbots make for good UX, but management sure likes them.)
Why are they not calling APIs directly with strictly defined inputs and outputs like every other internal application?
The story for MCP just makes no sense, especially in an enterprise.
This is obviously not what it is. If I give you APIGW would you be able to implement an MCP server with full functionality without a large amount of middleware?
Sorry, could you rephrase that?
It supports OAuth, IIRC. But I suppose the internal chatbot itself would require auth, and pass that down to the tools it calls.
I keep getting hung up on securely storing and using secrets with CLI vs MCP. With MCP, you can run the server before you run the agent, so the agent never even has the keys in its environment. That way. If the agent decides to install the wrong npm package that auto dumps every secret it can find, you are less likely to have it sitting around. I haven’t figured out a good way to guarantee that with CLIs.
And in a skill, I can store the secret in the skill itself, or a secure storage the skill accesses, and the agent never gets to see the secret.
Sure, if I want my agents to use naked curl on the CLI, they need to know secrets. But that's not how I build my tools.
I often just put direct curl commands in a skill, the agent uses that, and it works perfectly for custom API integrations. Agents are perfectly capable of doing these types of things, and it means the LLM just uses a flexible set of tools to achieve almost anything.
I think this is the best of both worlds. Design a sane API (that is easy to consume for both humans and agents), then teach the agents to use it with a skill.
But I agree with the author on custom CLI tooling. I don’t want to install another opaque binary on my machine just to call some API endpoints.