And again, the most convoluted setup for development with an example that fails to demonstrate why you should adopt such practice. It’s like doing a GDB demo with an hello world program. Or doing Linux From Scratch to show how you can browse the web.
The goal of software engineering is not to write code faster. Coding is itself a translation task (and a learning workflow, as you can’t keep everything in your head). What you want is the power of decision, and better decision can be made with better information. There’s nothing in the setup that helps with making decision.
There are roughly six steps in software engineering, done sequentially and iteratively. Requirements gathering to shape the problem, Analysis to understand it, Design to come up with a solution, Coding to implement it, Testing to verify the solution, and Maintenance to keep the solution working. We have methods and tooling that help with each, giving us relevant information based on important parameters that we need to decide upon.
LLMs are example generators. Give it a prompt and it will gives the answer that fits the conversation. It’s an echo chamber powered by a lossy version of the internet. Unlike my linting tool which will show me the error when there’s one and not when I tell it to.
ADDENDUM
It's like an ivory tower filled with yes-men and mirrors that always reply "you're the fairest of them all". My mind is already prone to lie to itself. What I need most is tooling that is not influenced by what I told it, or what others believe in. My browser is not influencing my note taking tool, telling it to note down the first two results it got from google. My editor is not telling the linter to sweep that error under a virtual rug. And QA does not care that I've implemented the most advanced abstraction if the software does not fit the specs.
Just yesterday I was reading a critique of MCP that specifically mentioned the GitHub MCP server as being harder to use (from model perspective) and requiring more tokens than having the agent execute git commands directly. I am surprised to see it listed here and also surprised to see two different web search servers and the time one. I would appreciate more detail from the author about the utility of each MCP server—overloading an agent with servers seems like it could be counterproductive.
Not sure how things are with Copilot, but with Claude Code a good alternative for MCP is in some cases old fashioned command line tools.
GitHub has gh, there's open source jira-cli, Cloudflare has wrangler and so on. No configuration needed, just mention on the agent doc that this kind of tool is available. Likely it will figure out the rest.
And if you have more complicated needs, then you can combine the commands, add some jq magic, put to package.json and tell agent to use npm run to execute it. Can be faster than doing it via multiple MCP calls.
Really cool article. Personally I think the really cool bit about MCP is that you can very easily write your own server which can talk to the db or call various APIs. That server can run locally and be used by GitHub Copilot for answering questions and executing tasks.
I also find it useful in a tight corporate environment where it’s more difficult to get a dedicated LLM API key. You can easily do POCa with what every dev has access to.
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[ 31.4 ms ] story [ 144 ms ] threadThe goal of software engineering is not to write code faster. Coding is itself a translation task (and a learning workflow, as you can’t keep everything in your head). What you want is the power of decision, and better decision can be made with better information. There’s nothing in the setup that helps with making decision.
There are roughly six steps in software engineering, done sequentially and iteratively. Requirements gathering to shape the problem, Analysis to understand it, Design to come up with a solution, Coding to implement it, Testing to verify the solution, and Maintenance to keep the solution working. We have methods and tooling that help with each, giving us relevant information based on important parameters that we need to decide upon.
LLMs are example generators. Give it a prompt and it will gives the answer that fits the conversation. It’s an echo chamber powered by a lossy version of the internet. Unlike my linting tool which will show me the error when there’s one and not when I tell it to.
ADDENDUM
It's like an ivory tower filled with yes-men and mirrors that always reply "you're the fairest of them all". My mind is already prone to lie to itself. What I need most is tooling that is not influenced by what I told it, or what others believe in. My browser is not influencing my note taking tool, telling it to note down the first two results it got from google. My editor is not telling the linter to sweep that error under a virtual rug. And QA does not care that I've implemented the most advanced abstraction if the software does not fit the specs.
https://browsermcp.io
It really feels magical when the AI agent can browse and click around to understand the problem at hand
Also, sometimes an interactive command can stop agents from doing things. I wrote a small wrapper to always return so agents never stop from working
https://github.com/mohsen1/agentshell
GitHub has gh, there's open source jira-cli, Cloudflare has wrangler and so on. No configuration needed, just mention on the agent doc that this kind of tool is available. Likely it will figure out the rest.
And if you have more complicated needs, then you can combine the commands, add some jq magic, put to package.json and tell agent to use npm run to execute it. Can be faster than doing it via multiple MCP calls.