Ask HN: Who is using MCP in production?

In general the advantage of MCP would be the possibility for a fine grained control over the tools the agent is allowed to use. But unfortunately there is a myriad of nightmarish awful mcp servers around which are worse than direct API access or even a cli integration.

I wont advertise any commercial mcp I use but to give an example for a well designed and useful mcp server I could name the nixos mcp. Its useful because it bundles all the nix resources to one endpoint which is more efficient than web search and gives you better control over the sources.

https://github.com/utensils/mcp-nixos

Another one would be this filesystem mcp which is in my opinion to prefer over direct cli access. Of course this depends also on your general sandbox strategy but if you just use a generic docker image there are still many potentially dangerous binaries available and such an mcp can restrict the models capabilities.

https://github.com/modelcontextprotocol/servers/tree/main/sr...

And of course there are many service provider offering their mcp with its own llm / agent behind e.g. most web search provider. In this case you most likely already use an mcp without noticing it.

I hooked up claude code to Jira and Figma. This is more a condemnation of Jira than anything, but being able to interact with it in natural language in the terminal is a huge relief. Sadly, it's still limited in some ways. If it's available, it makes things a tiny bit easier than interfacing directly via api, so it's a nice to have.

Building an agent runtime at cadenya.com and MCP servers have been the hardest part to create adapters for. For a two year old spec, there's far too many permutations.

I recently created a planning applications scraper for every council/local planning authority in the UK, very much with Claude's help. As you can no doubt imagine, it's the type of task where the mechanics are far less important than everything surrounding them.

Getting things like this to work well, for me, often boils down to finding out the things I don't know that I don't know in as little time as possible. I worked with Claude to create a basic debugging front-end as part of that - for my own use, inspecting the data, looking for patterns - when, largely on a whim, I asked Claude to create an MCP interface on top: last run status, what's looking good, whats not, where are the coverage gaps in the individual data points.

And I added the MVP to Claude and would check in regularly to ask how things were going.

And then - I'd ask for some nuance about why something doesn't look quite right. Or what happened to councils in general last night? And - out of nowhere, not being prompted or asked - Claude would query the MCP alongside looking at the code. Immediate production-grade insight while I'm debugging from the comfort of my chair.

And I thought "Oh."

And "this is really quite good," because the key detail is, I think, if an MCP is good and works well, and relevant to what you're doing every day at that point in time, it's yet another addition to your toolbox.

And that got me thinking about how I can do the same for my 9-5 users.

I built a small MCP service tool to my own learning; it retrieves study materials and course content, enabling me to learn through voice-based interactive conversations with ChatGPT. logs my learning data, which I can then retrieve and analyze to assess my overall progress.

I use fff [1] For the performance improvements when reading files. I prefer in most scenarios to use skills.

(1) https://github.com/dmtrKovalenko/fff

We have it, a fresh tool that landed recently in prod. Makes it easier to communicate with the app resources. And it's a standardised way for agents to talk to a remote resource, easier for them to understand what is available and how to use it.

Read more about mcp tools/resources/prompts.

Caudena released some of its enterprise capabilities within MCP last year, and made them available to the consumer market this year at https://mcp.caudexcatena.com/.

We have received very good feedback from clients across the enterprise and consumer segments, they really like it.

I used one for an AI tool that allows people to report bugs/feature request directly.

It searches to make sure it isn't a duplicate, writes up the ticket, then submits it.

Because it's a production tool, we want the cheapest possible one without it being too inaccurate. If you used a API etc, you'd end up building what's effectively a MCP-like adapter on top of it anyway so it could communicate in natural language instead of dealing with JSON and such.

Linear's MCP is also very clean and well designed, probably one of their core advantages over, say, Jira. I wouldn't know what the API looks like because the MCP works great.

MCP is still the main way I use Cursor with enterprise systems like Glean, Jira, and Port. Other than that, everything, especially you GitHub, is moved to CLIs (+skills as needed)

I find clis or calling apis directly to be waaay cheaper and faster. The only mcp i use at this point is the jira mcp only because i set it up a while ago and it's been there for a long time now

many things once needed a MCP server now could be easily done via using CLI by agent

i’m a day or two from releasing my programming language’s new mcp

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