[CuriouslyC]

MCP is less discoverable than a CLI. You can have detailed, progressive disclosure for a CLI via --help and subcommands.

MCPs needs to be wrapped to be composed.

MCPs needs to implement stateful behavior, shell + cli gives it to you for free.

MCP isn't great, the main value of it is that it's got uptake, it's structured and it's "for agents." You can wrap/introspect MCP to do lots of neat things.

[Eldodi]

"MCP is less discoverable than a CLI" -> not true anymore with Tool_search. The progressive discovery and context bloat issue of MCP was a MCP Client implementation issue, not a MCP issue.

"MCPs needs to be wrapped to be composed." -> Also not true anymore, Claude Code or Cowork can chain MCP calls, and any agent using bash can also do it with mcpc

"MCPs needs to implement stateful behavior, shell + cli gives it to you for free." -> having a shell+cli running seems like a lot more work than adding a sessionId into an MCP server. And Oauth is a lot simpler to implement with MCP than with a CLI.

MCP's biggest value today is that it's very easy to use for non-tech users. And a lot of developers seem to forget than most people are not tech and CLI power users

[CuriouslyC]

Just to poke some holes in this in a friendly way:

* What algorithm does tool_search use?

* Can tool_search search subcommands only?

* What's your argument for a harness having a hacked in bash wrapper nestled into the MCP to handle composition being a better idea than just using a CLI?

* Shell + CLI gives you basically infinite workflow possibilities via composition. Given the prior point, perhaps you could get a lot of that with hacked-in MCP composition, but given the training data, I'll take an agent's ability to write bash scripts over their ability to compose MCPs by far.

[prohobo]

"MCP is less discoverable than a CLI" - that doesn't make any sense in terms of agent context. Once an MCP is connected the agent should have full understanding of the tools and their use, before even attempting to use them. In order for the agent to even know about a CLI you need to guide the agent towards it - manually, every single session, or through a "skill" injection - and it needs to run the CLI commands to check them.

"MCPs needs to implement stateful behavior" - also doesn't make any sense. Why would an MCP need to implement stateful behavior? It is essentially just an API for agents to use.

[CuriouslyC]

If you have an API with thousands of endpoints, that MCP description is going to totally rot your context and make your model dumb, and there's no mechanism for progressive disclosure of parts of the tool's abilities, like there is for CLIs where you can do something like:

tool --help

tool subcommand1 --help

tool subcommand2 --help

man tool | grep "thing I care about"

As for stateful behavior, say you have the google docs or email mcp. You want to search org-wide for docs or emails that match some filter, make it a data set, then do analysis. To do this with MCP, the model has to write the files manually after reading however many KB of input from the MCP. With a cli it's just "tool >> starting_data_set.csv"

[prohobo]

This is a design problem, and not something necessarily solved by CLI --help commands.

You can implement progressive disclosure in MCP as well by implementing those same help commands as tools. The MCP should not be providing thousands of tools, but the minimum set of tools to help the AI use the service. If your service is small, you can probably distill the entire API into MCP tools. If you're AWS then you provide tools that then document the API progressively.

Technically, you could have an AWS MCP provide one tool that guides the AI on how to use specific AWS services through search/keywords and some kind of cursor logic.

The entire point of MCP is inherent knowledge of a tool for agentic use.

[troupo]

> like there is for CLIs where you can do something like

Well, these will fail for a large amount of cli tools. Any and all combinations of the following are possible, and not all of them will be available, or work at all:

    tool                    some tools may output usage when no arguments are supplied
    tool -h                 some tools may have a short switch for help
    tool --help             some tools may have a long switch for help
    tool help               some tools may have help as a subcommand
    tool command            some tools may output usage for a command with no arguments
    tool command -h         some tools may have a short switch for command help
    tool command --help     some tools may have a long switch for command help
    tool help command       some tools may have a help command
    man tool                some tools may have man pages
    

examples:

    grep                    one-line usage and "type grep --help"
    grep -h                 one-line usage and "type grep --help"
    grep --help             extended usage docs
    man grep                very extended usage docs


    python                  starts interactive python shell
    python -h
    python --help           equivalent help output


    ps                      short list of processes
    ps -h                   longer list of processes
    ps --help               short help saying you can do, for example, `ps --help a`
    ps --help a             gives an extended help, nothing about a

    erl                     
    erl -h
    erl --help              all three start Erlang shell
    man erl                 No manual entry for erl

etc.

Not to say that MCPs are any better. They are written by people, after all. So they are as messy.

[BeetleB]

> that MCP description is going to totally rot your context and make your model dumb, and there's no mechanism for progressive disclosure of parts of the tool's abilities,

Completely false. I was dealing with this problem recently (a few tools, consuming too many tokens on each request). MCP has a mechanism for dynamically updating the tools (or tool descriptions):

https://code.claude.com/docs/en/mcp#dynamic-tool-updates

We solved it by providing a single, bare bones tool: It provides a very brief description of the types of tools available (1-2 lines). When the LLM executes that tool, all the tools become available. One of the tools is to go back to the "quiet" state.

That first tool consumes only about 60 tokens. As long as the LLM doesn't need the tools, it takes almost no space.

As others have pointed out, there are other solutions (e.g. having all the tools - each with a 1 line description, but having a "help" tool to get the detailed help for any given tool).

[medbrane]

>here's no mechanism for progressive disclosure of parts of the tool's abilities

In fact there is: https://platform.claude.com/docs/en/agents-and-tools/tool-us...

If the special tool search tool is available, then a client would not load the descriptions of the tools in advance, but only for the ones found via the search tool. But it's not widely supported yet.

[fennecbutt]

>man tool | grep "thing I care about"

Isn't the same true of filtering tools available thru mcp?

The mcp argument to me really seems like people arguing about tabs and spaces. It's all whitespace my friends.

[kordlessagain]

Nobody said anything about an API with thousands of endpoints. Does that even exist? I've never seen it. Wouldn't work on it if I had seen it. Such is the life of a strawman argument.

Further, isn't a decorator in Python (like @mcp.tool) the easy way to expose what is needed to an API, if even if all we are doing is building a bridge to another API? That becomes a simple abstraction layer, which most people (and LLMs) get.

Writing a CLI for an existing API is a fool's errand.

[CuriouslyC]

Cloudflare wrote a blog post about this exact case. The cloud providers and their CLIs are the canonical example, so 100% not a strawman.

[locknitpicker]

> Writing a CLI for an existing API is a fool's errand.

I don't think your opinion is reasonable or well grounded. A CLI app can be anything including a script that calls Curl. With a CLI app you can omit a lot of noise from the context things like authentication, request and response headers, status codes, response body parsing, etc. you call the tool, you get a response, done. You'd feel foolish to waste tokens parsing irrelevant content that a deterministic script can handle very easily.

[coldtea]

>"MCP is less discoverable than a CLI" - that doesn't make any sense in terms of agent context. Once an MCP is connected the agent should have full understanding of the tools and their use, before even attempting to use them. In order for the agent to even know about a CLI you need to guide the agent towards it - manually, every single session, or through a "skill" injection - and it needs to run the CLI commands to check them.

Knowledge about any MCP is not something special inherent in the LLM, it's just an agent side thing. When it comes to the LLM, it's just some text injected to its prompting, just like a CLI would be.

[Twirrim]

I'm using an MCP to enhance my security posture. I have tools with commands that I explicitly cannot risk the agent executing.

So I run the agent in a VM (it's faster, which I find concerning), and run an MCP on the host that the guest can access, with the MCP also only containing commands that I'm okay with the agent deciding to run.

Despite my previous efforts with skills, I've found agents will still do things like call help on CLIs and find commands that it must never call. By the delights of the way the probabilities are influenced by prompts, explicitly telling it not to run specific commands increases the risk that it will (because any words in the context memory are more likely to be returned).