alt Hacker NewsStateless MCP has recaptured my interest
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I think stateless-type MCP was already possible, eg my MCP Clock [https://github.com/firasd/mcpclock]:
> curl -s -X POST "https://mcpclock.firasd.workers.dev/mcp" -H "Content-Type: application/json" -H "Accept: application/json, text/event-stream" -d '{"jsonrpc":"2.0","id": 1,"method":"tools/call","params":{"name":"clock_get","arguments":{}}}' | grep '^data:' | sed 's/^data: //'| jq
{"result": {"content": [{"type": "text",
"text": "[\n {\n \"timezone\": \"UTC\",\n \"iso\": \"2026-08-05T04:44:41.707Z\",\n \"unixtime\": 1785905081\n },\n {\n \"timezone\": \"Alphadec\",\n \"alphadec\": \"2026_P4A0_466322\"\n }\n]"
}]},"jsonrpc": "2.0", "id": 1}
1) You're a developer 2) On a laptop 3) With a shell open inside an agentic coding harness (Claude Code, Codex CLI, Cursor) 4) Working on a software project
That's maybe 2% of AI usage.
The other 98% is: Someone on the ChatGPT iOS app asking a question on the subway; Someone in Claude.ai web chatting about their calendar; Someone using ChatGPT Desktop to summarize their Notion; A non-developer using AI in a browser at work; Voice mode on a phone; An embedded chat widget on some company's website...
It is amazing that folks getting paid several hundred thousand dollars a year...reinvented RPC-over-HTTP/JSON. You too, fellow web developers, are smart enough to work at Anthropic.
I would love to see a proper engineering post-mortem for how this happened.
In retrospect, stateful MCP was clearly wrong.
This essentially makes MCP just another REST API endpoint, and lets you use the same infrastructure you already have set up for REST APIs (like load balancers, API gateways, progressive rollouts, etc).
MCP is nice, but does your agent use it without you explicitly asking it to do so?
From my experience, instructions in `.md` files aren't enough. In fact, Claude seems to prefer the shell over even its own built-in tools (e.g. `read`, `edit` etc.) See: https://github.com/anthropics/claude-code/issues/19649. It's like an old dog that needs to be tricked to learn new tricks.
My main issue with MCP servers has been context bloat. Skills have progressive disclosure, and allow me to turn off auto invocation with something like this in the SKILL.md file:
disable-model-invocation: true
Most MCP servers still bloat up my context even when I am not using them at all. Why can't they be configured to allow me to keep them out unless I really need to use them?
I still don't get MCP. Most likely because I didn't really check but the first feeling is creating a problem to solve a problem that doesn't exist in the first place.
MCPs are very exciting. They now support IFrames (MCP Apps) as well.
Now that the models are getting better, you can trust them to book barber appointments, order groceries, manage your calendar, etc etc. It's significantly lazier to ask an agent to do something than manually doing it. Especially with voice to text.
I'm predicting that chat bots will auto-discover MCPs from trusted websites and make life much easier.
In our startup, we build harness that allows people to call endpoints like the ones that MCP supports. we looked it as 2 class of problems. One is sources like databases where the protocol is not http/json/rpc. Here we support MCP based calling. then there are ones where the protocol is just http/json/rpc - say calling shopify or linear or others. Now the harness itself comes with a generic http execution engine. So if you know the endpoint, auth and operation - the harness converts this into a yaml structure for internal purpose and directly calls the endpoint. all info that LLM needs like how the endpoints are, what can be passed and how to check response etc., is all english defined and stored in yaml. Now the harness uses this info to expose these to LLM and when LLM wants to makes a tool call that needs to call the http endpoint the http engine in the harness executes it on behalf of it and gives response back to LLM. this way every server dont have to expose and maintain an MCP server to expose them to LLM. allow us to connect to any endpoint and teams dont have to maintain and scale one more info.
Not sure whats the downside of this approach is but its serving us well so far.
But is it composable like cli? The main issue to be with MCP is the entire response ends up in the context window. Whereas a decent harness and agent is usually going to pipe together and filter many tools in one long command without spending all the extra tokens.
we invented a stateful protocol, discovered state is hard to scale, stripped it out, and arrived at "just send a POST request." the REST crowd has been smugly waiting for this moment for 20 years.
> I couldn’t find a great CLI tool for interactively probing an MCP server
What about mcp-inspector? It’s a nice tool, can be used interactively, can be used as a CLI.
> and then became somewhat eclipsed by Skills (another Anthropic invention)
I thought it was The Browser Company’s invention?
https://claude.com/blog/skills
> Introducing Agent Skills
> October 16, 2025
I can find articles and Reddit posts about Dia Browser’s Skills from July 2025.
Hi Simon and HN checkout https://rmcp.dev - it's part of BuiltWith's MCP discovery and lets you sniff around all of the MCP servers (remote at least) that we've discovered - allowing you to connect to them etc. and see what they do. Thought it might be relevant vis your mcp-explorer.
Yeah, there's a ton of great improvements in 7-28. I'm personally excited about what you posted about, but also with [tasks](https://blog.modelcontextprotocol.io/posts/2026-07-28-releas...) being officially adopted.
I createad an MCP server for my company I worked at for almost a year now with all Dynamic Client Registration (DCR). We just ignored the state as with most other MCPs wre doing it, just used it for logging.
I'm glad MCP is getting simpler
a few months ago I tried to implement an MCP server from scratch in python (instead of using the existing reference implementation) and I could not get it to work reliably across clients
Stateless MCP was already possible before this and made sense for whole classes of use cases where it helps to have a remote fleet of servers.
Wrote about this back in March: https://chrlschn.dev/blog/2026/03/mcp-is-dead-long-live-mcp/
MCP is going to be a foundational piece of enterprise agent infra.
Maybe someone could set up a CLI tool for agents such that you can give them a shell but they use this CLI tool instead of raw curl.
Like a tool where the AI can only call out to certain APIs based on a config file the agent cannot change.
That way you can leverage all the shell knowledge agents already have while still limiting what network calls they can make, and you wouldn't have to set up a server to use an agent.
Interesting that a date format is used for MCP-Protocol-Version.
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In this thread I’m seeing a couple of the typical skill arguments. Skills don’t pollute the context window and, skills are more flexible and composable.
In this specific post the SQL example gets around this by being super low-level, inherently composable, and low context consumption itself. I’m not sure this works for everyone.
Looking at the Linear MCP for example, they basically just want to expose an API surface and provide 32 MCP tools as a result of that. My agents do run into composability problems here all the time and fall back to the tool they included which allows you to run arbitrary GraphQL. This takes a couple tries usually. I don’t really know what the sensible alternative to this is.
Cloudflare switched to “code mode” which basically provides 2 MCP tools: search and execute. Both take a TS arrow function, the first allows you to programmatically search a TS api spec, the second to compose and run the methods in that spec.
I think this is a very interesting approach, and certainly better than providing the ~1000 actions as MCP tools but I feel the jury is still out on exactly how well this pans out.