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- Hacker News
- MCP's are great for adoption of your service outside of developer circles. Non Technical users can click a link, trigger an OAuth flow and authenticate. You can't do that with API. API still is better for developers using a AI via a CLI but MCP is easier adoption for web or desktop based clients.by hkchad
- For what I am using it for, I recently wrote a little web app called Afterfeed [0] that lets you view backups from social media websites as a single unified timeline.
To put this into perspective, I was a voracious social media user for decades. For me, this is 25 years of data, close to 100,000 posts, spanning everything from LiveJournal in 2001 to Mastodon in 2026. But when put together, it is a timeline of my life.
As kind of a last minute idea, I added an MCP server because why not? Once I wired an LLM up to it, I could ask introspective questions and find new ways of looking at myself and my own history. Simple things like "what was I doing 10 years ago around this time?" to more complex things like researching past thought patterns.
- My context is realtime visual effect creation, but I’ve used MCP extensively in my (native) custom harness as a way to drive in—app UI updates and of course bidirectional state queries, including framebuffer capture for closed loop verification.
I feel like CLI would probably work too but then I’d end up implementing something similar. That being said, I’ve been bitten by the usual suspects: too many tools will cause context windows to grow quickly and some agents will sometimes skim through a subset of the tool list without querying the entire thing, causing incorrect behavior.
If you have tokens to burn I invite you to check out the source code see how extensively it’s being used: https://github.com/sxp-studio/subjective-zero
(video to see the MCP in action, it’s a bit long so feel free to skip: https://www.youtube.com/watch?v=DcI1tsPJ8eM)
Another kind of cool use of MCP that I’ve encountered is actually from… the French government! They do it for their open data initiative: https://github.com/datagouv/datagouv-mcp
by tasoeur - Yes, quite a few uses in prod, main use case being abstracting API access for agents. In our case these are mostly in-house MCP servers, purpose built for the given agent.
Why MCP instead of CLI or agent accessing API directly?
Direct access (Curl/own small function): This requires agent to have full understanding of the API spec. Yes, context can be protected using progressive disclosure, but this essentially means agent needing to understand the API again and again, before every use in that context. Also, a typical API spec may or may not be agent-friendly. If there are nuances when calling an endpoint, where do we put these? Into OAS description? Works, but clunky.
CLI: It works beautifully, especially when a 3rd party CLI already exists for a complex backend. Assumes a well documented, agent friendly CLI, most CLIs are designed for human or CI/CD consumption. Talking about MCP taking up too much context, think about agent starting with my_cli --help, and going down through the switches and parameters one at a time to figure out how the CLI should be called. Less of a problem when calling a well know CLI (e.g. aws), but anything more niche (or custom) requires multiple turns to compose the final CLI command.
MCP: Has its issues, but offers an agent-native solution. Everything agent needs to know about a tool becomes available at once. In an enterprise environment MCPs can be served through an MCP Gateway, providing governance and permission management, this is quite contrast against running a CLI that requires agent to have execute permissions in its shell.
I must mention that we also utilise lazy-loading of MCPs. In use cases where 10s of tools needs to be loaded, only the most common ones are pre-loaded, then agent kernel connects the others as an when needed, and release them after a timeout (in case of long running sessions). This keeps the context lean.
by cagz - 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
by navigate8310 - 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.
by MitziMoto - 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.
by 5ersi - 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.
by Aldipower