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Ask HN: Who is using MCP in production?

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Re: Ask HN: Who is using MCP in production?

#111
post #97

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 cor…

FWIW, it appears that using the gh cli is much more token efficient than MCP so that's a plus.

https://medium.com/@ravi.madabhushi/mcp-is-up-to-32-more-exp...

Re: Ask HN: Who is using MCP in production?

#112
Running a bunch alongside APIs for suite of internal services where domain experts are using agents to setup environments for their work. Getting them to update their LLMs to a new SDK version every time we iterate API capabilities is all the headache you'd imagine. The MCP avoids inconsistent user reports of capabilities.

The specifics, now user can ask, "Provision 200Gi PostgreSQL 18 with PostGIS 3.6 and then copy over our most recent Texas set of layers."

Behind the scene Talos, k8s, NiFi, and bunch of things go to work. Big productivity boost vs. the users needing to login to bunch of provisioning web interfaces which is the other method we publish that avoids version drift issues.

Re: Ask HN: Who is using MCP in production?

#113

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-t…

This. We also thought MCP was useless, but now the workflows by ChatGPT and Claude are so good; it's as good as installing an app from the App Store. We launched MCP for our SaaS and immediately found users using it. API and MCP all are just buzzwords for customers. What they care about is whether they can get tasks done in our SaaS using Claude or ChatGPT. If that happens, it's a success.

Do you not expose a standard REST api?

Re: Ask HN: Who is using MCP in production?

#114

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.…

That's an awesome example! I'll definitely make use of your repo and idea for my German classes. Did you use a specific OCR method? I'm trying to get something better than tesseract :(

I used Mistral 3. It costed me literally pennies. My workflow was kinda rudimentary but it worked really well. I suggest take a look at this https://github.com/docling-project/docling as well.

Re: Ask HN: Who is using MCP in production?

#115
post #97

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 cor…

FWIW, it appears that using the gh cli is much more token efficient than MCP so that's a plus. https://medium.com/@ravi.madabhushi/mcp-is-up-to-32-more-exp...

I see benchmarks a lot working on this, and I honestly think there’s too many things to cover in this comment but: - this article doesn’t enable tool search for starters where tokens start at near zero for MCP too - even with the above, talks of the raw token count don’t consider that for very long trajectories the prompt cache amortises much of this cost, and a feature of MCP is you can also customise the tools to a select subset, and that’s powerful - you can add tool confirmations for write/destructive actions trivially - the old training data on CLIs is good but actually for new features the fact MCP advertises them is also a bonus, but that takes longer to see, MCP isn’t that old

There are many more aspects, but even with GitHub MCP, where the agent has a huge amount of pre-training on the CLI, there are still reasons why people use it extensively - you don’t hear it a lot here but suffice to say usage never stopped growing to this day.

I think if CLIs are working for you then good, I also like our cli and did some work on it too, like adding gh skill subcommand.

Do use what works, and measure and improve - but this article is not doing justice to the MCP server IMHO.

Re: Ask HN: Who is using MCP in production?

#116
I use it for fully auditable agentic compliance work - many of my customers already used MCPs for their compliance work, and recent changes made that fully auditable. I have to admit that it's much easier to use and work on compliance this way than using the agent built into the app.

https://auditbadger.com/auditable-agentic-compliance/

Re: Ask HN: Who is using MCP in production?

#118
I assume the target audience for the question is developers. That will generate many responses along the lines of ”just use a CLI”.

The current, main driver I see for MCP servers are non-technical people who want to securely connect data sources to their AI application.

Re: Ask HN: Who is using MCP in production?

#120
In many cases, you can partition data at rest (eg parquet) on the user access key, then load it once into an agent container at runtime. In this way, one authentication pass provides access to all data in a sandbox, versus repeatedly authenticating against MCP calls. It also just reduces the connective tissue between the harness and the data. There are obviously drawbacks in that you aren’t leveraging real time APIs, but it works well for most business use cases.
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