> Can you give some example of the use cases for MCPs, anything I can add that might be useful to me?

How "useful" a particular MCP is depends a lot on the quality of the MCP but i've been slowly testing the waters with GitHub MCP and Home Assistant MCP.

GH was more of a "go fix issue #10" type deal where I had spent the better part of a dog-walk dictating the problem, edge cases that I could think of and what a solution would probably entail.

Because I have robust lint and test on that repo, the first proposed solution was correct.

The HomeAssistant MCP server leaves a lot to be desired; next to no write support so it's not possible to have _just_ the LLM produce automations or even just assist with basic organization or dashboard creation based on instructions.

I was looking at Ghidra MCP but - apparently - plugins to Ghidra must be compiled _for that version of ghidra_ and I was not in the mood to set up a ghidra dev environment... but I was able to get _fantastic_ results just pasting some pseudo code into GPT and asking "what does this do given that iVar1 is ..." and I got back a summary that was correct. I then asked "given $aboveAnalysis, what bytes would I need to put into $theBuffer to exploit $theorizedIssueInAboveAnalysis" and got back the right answer _and_ a PoC python script. If I didn't have to manually copy/paste so much info back and forth, I probably would have been blown away with ghidra/mcp.

Basically, my philosophy with agents is that I want to orchestrate agents to do stuff on my computer rather than use a UI. You can automate all kinds of stuff, like for instance I'll have an agent set up a storybook for a front-end, then have another agent go through all the stories in the storybook UI with the Playwright MCP and verify that they work, fix any broken stories, then iteratively take screenshots, evaluate the design and find ways to refine it. The whole thing is just one prompt on my end. Similarly I have an agent that analyzes my google analytics in depth and provides feedback on performance with actionable next steps that it can then complete (A/B tests, etc).

> anything I can add that might be useful to me?

This totally reads to me like you're prompting an LLM instead of talking to a person

Here’s an example https://contextsync.dev/

You can now let ChatGPT interact with any service that exposes an API, and then additionally provides an MCP server for to interact with the API

Playwright mcp lets the agent operate a browser to test the changes it made, it can click links, execute JavaScript and analyse the dom

The most useful ones are memory and sequential thinking. Imo

At my work were replacing administrative interfaces/workflows with an MCP to hit specific endpoints of our REST API. Jury is still out on whether or not it will work in practice but in theory if we only need to scaffold up MCP tools we save a good chunk of dev time not building out internal tooling.