Ask HN: Who is using MCP in production?
I've been following MCP since it first came out. It got a lot of attention early on, but I haven't come across many people using it in production. I may simply have missed them. If you're using MCP in production, what are you using it for? What advantages have you found over a normal API or direct tool integration or just CLI?
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.
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.
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
- boredumb
We offer MCP and then consume it with our in-app assistant to go from a non technical prompt to a series of what is essentially API calls they can automate for themselves for repetitive tasks or things that require a few screens to accomplish can be done from the assistant widget itself, etc.
- Aldipower
We use MCP in production since March 26 for user-facing endurance sports analytics and planning that integrates directly with the MCP host, in this case the chat interfaces of ChatGPT, Claude, Grok, Perplexity.ai, Mistral, you name it. All of those let users add either custom MCP servers, which users can do with a simple explanation. To connect Claude for example: https://www.tredict.com/faq/connect-claude-web-with-tredict/
This way users can use Claude to create and push workouts with their Garmin devices based on a prior analysis.
We also have a ChatGPT App, which uses MCP in the background: https://chatgpt.com/plugins/plugin_asdk_app_69aef5b699a08191...
The point here is, MCP is the backbone behind the product and enables regular users to do things without knowing anything about it.
The whole "is MCP useless?" discussion is totally pointless from a regular consumer-user perspective, they even do not know what MCP is sometimes. The Tredict ChatGPT App connects with one click and a simple oauth flow. That's it.
- cgarvis
MCP is for when your end user (the one driving an LLM Agent) is non technical. Most non technical people are not going to install a CLI on their computer. Most are not going to be driving the LLM agent via a terminal.
It also very helpful when you need auth. MCP OAuth with CIMD makes it easy instead of cumbersome process of generating API Keys.
If you are technical and already using CLIs, then MCP doesn't give you much.
- 5ersi
MCPs are diminishing in value a bit, because AI Agents are getting smarter about using API/CLIs. For example, I use gh cli via Claude instead of their MCP, because I already had cli setup, so no need to use MCP.
MCPs can potentially have great value if they cross multiple sources and combine results. For example at work we use an in-house MCP for log/metrics search across five different (legacy) systems. It finds correlation across events in different system within minutes.
- konart
My company (I'm not part of the team that's responsible for anything AI related) has MCP's for Jira, Confluence, Mattermost fork and a few other integrations.
I have no idea about MCP vs API question but I always assumed that the whole point of MCP was an abstration between the model and the tool\service.
As in model does not have to know about a certain API (not to mention a particular version of it) to work with a service.
- MitziMoto
We use MCP in production for our customer facing voice agents. Our custom MCP server defines tools and resources that the voice agents need access to to interact with our customers. (E.g. scheduling appointments, checking order status, etc).
Now we can point any voice agent platform we choose -- eleven labs, vapi, pipecat, whatever -- at our custom MCP server and it instantly has an understanding of the tools available, their inputs, and how to use them.
Compared to the alternatives everyone on HN champions, like clis and APIs, this is a no brainer. I'm honestly not even sure what the realistic alternative would even be.
Am I supposed to package and distribute a cli to ElevenLabs and ask them to use it? Give them a full API spec to implement for me?
I give them an endpoint and credentials and their platform instantly knows how to talk to mine. No one at ElevenLabs knows or cares about our implementation details.
HN has trouble seeing past the "developer in a terminal coding with Claude Code" use case for using AI. Real production agents have use cases that are very different!
When you don't own every piece of an integration with another system, there needs to be a well defined standard. That's what MCP provides.
- navigate8310
I OCR'd my Chinese textbooks and made a stateless MCP that allows me to ground my Chinese language studies according to the textbook only. With this I can start a quiz, understand differences between words that have similar meanings knowing no extra grammar is fed when reviewing. I specifically use it with glm 5.3 as it is the most language specific LLM that understands nuances.
Here's the repository: https://github.com/iodize6399/xuexi-keben
And here's the server itself: https://keben.555420.xyz
- throwup238
I use MCP for two types of things: ones where I need to give access to the memory of the process like an MCP server to inspect a Qt gui, and ones where I need to give authenticated access to my data to ChatGPT/Claude web/mobile apps. For these uses CLIs would be too cumbersome in use or auth.
For the latter, Cloudflare Tunnels + Zero Trust + Github SSO means securing them is rather easy and with the recent cf cli the agent does all the work. Their native support for base64 encoded images is also helpful to “encourage” the LLM to look at stuff without constant prodding.