31–40 of 231 posts
MCP is fine, particular remote MCP which is the lowest friction way to get access to some hosted service with auth handled for you. However, MCP is context bloat and not very good compared to CLIs + skills mechanically. With a CLI you get the ability to filter/pipe (regular Unix bash) without having to expand the entire tool call every single time in context. CLIs also let you use heredoc for complex inputs that are…
It’s much easier for users to find what exactly a model can do with your app over it compared to building a skill that would work with it since clients can display every tool available to the user. There’s also no need for the model to setup any environment since it’s essentially just writing out a function, which saves time since there’s no need to setup as many virtual machine instructions.
It obviously isn’t as useful in development environments where a higher level of risk can be accepted since changes can always be rolled back in the repository.
If I recall correctly, there’s even a whole system for MCP being built, so it can actually show responses in a GUI much like Siri and the Google Assistant can.
Earlier quoted context omitted.
All the code I work on now has an MCP interface so that the LLM can debug more easily. I'd argue it is as important as the UI these days. The amount of time it has saved me is unreal. It might be worth investing a very small amount of your time in it to see if it is a good fit. Even a poor protocol can provide useful functionality.
Our workflows must be massively different. I code in 8 languages, regularly, for several open source and industry projects. I use AI a lot nowadays, but have never ever interacted with an MCP server. I have no idea what I'm missing. I am very interested in learning more about what do you use it for.
> I have no idea what I'm missing.
- Do you work in a team context of 10+ engineers?
- Do you all use different agent harnesses?
- Do you need to support the same behavior in ephemeral runtimes (GH Agents in Actions)?
- Do you need to share common "canonical" docs across multiple repos?
- Is it your objective to ensure a higher baseline of quality and output across the eng org?
- Would your workload benefit from telemetry and visibility into tool activation?
I’ve always felt like MCP is way better suited towards consumer usage rather than development environments. Like, yeah, MCP uses a lot of a context window, is more complex than it should be in structure, and it isn’t nearly as easy for models to call upon as a command line tool would be. But I believe that it’s also the most consumer friendly option available right now. It’s much easier for users to find what exactly…
> If I recall correctly, there’s even a whole system for MCP being built, so it can actually show responses in a GUI much like Siri and the Google Assistant can
Earlier quoted context omitted.
All the code I work on now has an MCP interface so that the LLM can debug more easily. I'd argue it is as important as the UI these days. The amount of time it has saved me is unreal. It might be worth investing a very small amount of your time in it to see if it is a good fit. Even a poor protocol can provide useful functionality.
Our workflows must be massively different. I code in 8 languages, regularly, for several open source and industry projects. I use AI a lot nowadays, but have never ever interacted with an MCP server. I have no idea what I'm missing. I am very interested in learning more about what do you use it for.
I have been working on a system using a Fjall datastore in Rust. I haven't found any tools that directly integrate with Fjall so even getting insight into what data is there, being able to remove it etc is hard so I have used https://github.com/modelcontextprotocol/rust-sdk to create a thin CRUD MCP. The AI can use this to create fixtures, check if things are working how they should or debug things e.g. if a query is returning incorrect results and I tell the AI it can quickly check to see if it is a datastore issue or a query layer issue.
Another example is I have a simulator that lets me create test entities and exercise my system. The AI with an MCP server is very good at exercising the platform this way. It also lets me interact with it using plain english even when the API surface isn't directly designed for human use: "Create a scenario that lets us exercise the bug we think we have just fixed and prove it is fixed, create other scenarios you think might trigger other bugs or prove our fix is only partial"
One more example is I have an Overmind style task runner that reads a file, starts up every service in a microservice architecture, can restart them, can see their log output, can check if they can communicate with the other services etc. Not dissimilar to how the AI can use Docker but without Docker to get max performance both during compilation and usage.
Last example is using off the shelf MCP for VCS servers like Github or Gitlab. It can look at issues, update descriptions, comment, code review. This is very useful for your own projects but even more useful for other peoples: "Use the MCP tool to see if anyone else is encountering similar bugs to what we just encountered"
Earlier quoted context omitted.
MCP loads all tools immediately. CLI does not because it’s not auto exposed to the agent, got have more control of how the context of which tools exist, and how to deliver that context.
You can solve the same problem by giving subsets of MCP tools to subagents so each subagent is responsible for only a subset of tools. Or...just don't slam 100 tools into your agent in the first place.
But I can do them with CLI so that's a negative for MCP?
This came up in recent discussions about the Google apps CLI that was recently released. Google initially included an MCP server but then removed it silently - and some people believe this is because of how many different things the Google Workspace CLI exposes, which would flood the context. And it seemed like in social media, suddenly a lot of people were talking about how MCP is dead. But fundamentally that doesn’…
MCP loads all tools immediately. CLI does not because it’s not auto exposed to the agent, got have more control of how the context of which tools exist, and how to deliver that context.
Earlier quoted context omitted.
Our workflows must be massively different. I code in 8 languages, regularly, for several open source and industry projects. I use AI a lot nowadays, but have never ever interacted with an MCP server. I have no idea what I'm missing. I am very interested in learning more about what do you use it for.
> I have no idea what I'm missing. The questions I'd ask: - Do you work in a team context of 10+ engineers? - Do you all use different agent harnesses? - Do you need to support the same behavior in ephemeral runtimes (GH Agents in Actions)? - Do you need to share common "canonical" docs across multiple repos? - Is it your objective to ensure a higher baseline of quality and output across the eng org? - Would your wor…
Earlier quoted context omitted.
If AI is AI, why does it need a protocol to figure out how to interact with HTTP, FTP, etc.? MCP is a way to quickly get those integrations up and running, but purely because the underlying technology has not lived up to its hyped abilities so far. That's why people think of MCP as a band-aid fix.
Because protocols provide structure that increases correctness. It is not a guarantee (as we see with structured output schemas), but it significantly increases compliance.
So why MCP? Are there other protocols that will provide more correctness when trained? Have we tried? Maybe a protocol that offers more compression of commands will overall take up more context, thus offering better correctness.
MCP seems arbitrary as a protocol, because it kinda is. It doesn't >>cause>is<< a protocol is the reason it may increase correctness. Thus, any other protocol would do the same thing.