Show HN: Ask-human-mcp – zero-config human-in-loop hatch to stop hallucinations
53 comments
·June 5, 2025superb_dev
This site is impossible to read on my phone. Part of the left side of the screen is cut off and I can’t scroll it into view
tyzoid
Completely blank for me on mobile (javascript disabled)
rfl890
Switching to desktop mode fixed it for me
lobsterthief
Same here
banner520
I also have this problem on my phone
loloquwowndueo
- someone sets up an “ask human as a service mcp” - demand quickly outstrips offer of humans willing to help bots - someone else hooks up AI to the “ask human saas” - we now have a full loop of machines asking machines
lordmauve
Finally, the "AI" turns out to be 700 Indians. We now have the full loop of humans asking machines asking humans pretending to be machines. Civilisation collapses
olalonde
I built this - but mostly as a joke / proof-of-concept: https://github.com/olalonde/mcp-human
aziaziazi
Cool project! Naive question: does mechanical turk uses llm now?
TZubiri
This is pretty much already possible in any economy, but quite a waste.
Not much is stopping you from buying products from a retailer and selling them at a wholesaler, but you'd lose money in doing so.
threeseed
> an mcp server that lets the agent raise its hand instead of hallucinating
a) It doesn't know when it's hallucinating.
b) It can't provide you with any accurate confidence score for any answer.
c) Your library is still useful but any claim that you can make solutions more robust is a lie. Probably good enough to get into YC / raise VC though.
echollama
reasoning models know when they are close to hallucinating because they are lacking context or understanding and know that they could solve this with a question.
this is a streamlined implementation of a interanlly scrapped together tool that i decided to open-source for people to either us or build off of.
geraneum
> reasoning models know when they are close to hallucinating because they are lacking context or understanding and know that they could solve this with a question.
I’m interested. Where can I read more about this?
threeseed
> reasoning models know when they are close to hallucinating because they are lacking context or understanding and know that they could solve this with a question
You've just described AGI.
If this were possible you could create an MCP server that has a continually updated list of FAQ of everything that the model doesn't know.
Over time it would learn everything.
xeonmc
Unless there is as yet insufficient data for meaningful answer.
exclipy
Would be great if it pinged me on slack or whatsapp. I wouldn't notice if it simply paused waiting for the MCP call to return
spacecadet
Easy enough to do with smolagents and fastmcp, its 20 lines of code.
mgraczyk
If you are answering these questions yourself, why not just add something like this to your cursor rules?
"If you don't know the answer to a question and need the answer to continue, ask me before continuing"
Will you have some other person answer the question?
bckr
I’ve tried putting “stop and ask for help” in prompts/rules and it seems like Cursor + Claude, up to 3.7, is highly aligned against asking for help.
null
deadbabe
Having another person answer the question is pretty much the obvious route this will go.
mgraczyk
But then that means they are editing a markdown file on your computer? How is that meant to work?
I like the idea but would rather it use Slack or something if it's meant to ask anyone.
echollama
this is mainly meant as a way to conversate with the model while you are programming with it. This is not meant to pull questions to a team but more to pair program. a markdown file is best for syntax in an llm prompt and also just easiest to have open and answer questions with. If i had more time and could i would build an extension into cursor.
ramesh31
>If you are answering these questions yourself, why not just add something like this to your cursor rules?
What you are asking for is AGI. We still need human in the loop for now.
mgraczyk
What I'm describing is a human in the loop. It's just a different UX, one that is easier to use and closer to what the model is trained to use.
ramesh31
Human in the loop means despite your best efforts at initial prompting (which is what rules are), there will always be the need to say "no, that's wrong, now do this instead". Expecting to be able to write enough rules for the model to work fully autonomously through your problem is indeed wishing for AGI.
kordlessagain
The same technique can be had by creating a "universal MCP tool" for the LLM to use if it thinks the existing tools aren't up to the job. The MCP language calls these "proxies".
kjhughes
Cool conceptually, but how exactly does the agent know when it's unsure or stuck?
aziaziazi
I had the same question reading your post:
> (problem description) your agent […] makes confident assumptions
> (solution description) when it’s unsure
I read this as a contradiction: in one sentence you describe the problem as an agent being confident while hallucinating and in the next phrase the solution is that the agent can ask you if it’s unsure.
You tool is interesting but you may consider rephrasing that part.
Groxx
The same way it knows anything else.
So not at all, but that doesn't mean it's not useful.
kjhughes
I'll try to give you credit for more than dismissing my question off-hand...
Yes, it may not need to know with perfect certainty when it's unsure or stuck, but even to meet a lower bar of usefulness, it'll need at least an approximate means of determining that its knowledge is inadequate. To purport to help with the hallucination problem requires no less.
To make the issue a bit more clear, here are some candidate components to a stuck() predicate:
- possibilities considered
- time taken
- tokens consumed/generated (vs expected? vs static limit? vs dynamic limit?)
If the unsure/stuck determination is defined via more qualitative prompting, what's the prompt? How well has it worked?
Groxx
I don't believe[1] any of those are part of the MCP protocol - it's essentially "the LLM decided to call it, with X arguments, and will interpret the results however it likes". It's an escape hatch for the LLM to use to do stuff like read a file, not a monitoring system that acts independently and has control over the LLM itself.
(But you could build one that does this, and ask the LLM to call it and give your MCP that data... when it feels like it)
So you'd be using this by telling the LLM to run it when it thinks it's stuck. Or needs human input.
1: I am not anything even approaching deeply knowledgeable about MCP, so please, someone correct me if I'm wrong! There do seem to be some bi-directional messaging abilities, e.g. notification, but to figure out thinking time / token use / etc you would need to have access to the infrastructure running the LLM, e.g. Cursor itself or something.
threeseed
You are trying to control a system that is inherently chaotic.
You can probably get some where by indeed running a task 1000 times and looking for outliers in the execution time or token count. But that is of minimal use and anything more advanced than that is akin to water divining.
TZubiri
So we are just pushing the issue to another, less debuggable layer. Cool.
echollama
the reasoning aspect of most llms these days knows when its unsure or stuck, you can get that from its thinking tokens. It will see this mcp and call it when its in that state. Though this could benefit from some rules file to use it, although cursor doesn't quite follow ask for help rules, hence making this.
kjhughes
Does all thinking end up getting replaced by calls to Ask-human-mcp then? Or only thinking that exceeds some limit (and how do you express that limit)?
ddalex
Why wouldn't a rag-enabled ai be faster and better then humans at answering these documentation-grounded questions ?
rgbrenner
Sounds similar to `ask_followup_question` in Roo
spacecadet
If the model responds with an obvious incorrect answer or hallucination, start over. Rephrase your input. Consider what output you are actually after... Adding to original shit output wont help you.
conception
What sort of prompt are you using for this?
kordlessagain
The prompt is (mostly) built using the tool loads in the MCP server. In Python, the @mcp.tool() decorators provide the context of tool to the prompt, which is then submitted (I believe) with each call to the LLM.
throwaway314155
Not certain that your definition of hallucination matches mine precisely. Having said that, this is so simple yet kinda brilliant. Surprised it's not a more popular concept already.
While building my startup i kept running into the issue where ai agents in cursor create endpoints or code that shouldn't exist, hallucinates strings, or just don't understand the code.
ask-human-mcp pauses your agent whenever it’s stuck, logs a question into ask_human.md in your root directory with answer: PENDING, and then resumes as soon as you fill in the correct answer.
the pain:
your agent screams out an endpoint that never existed it makes confident assumptions and you spend hours debugging false leads
the fix:
ask-human-mcp gives your agent an escape hatch. when it’s unsure, it calls ask_human(), writes a question into ask_human.md, and waits. you swap answer: PENDING for the real answer and it keeps going.
some features:
- zero config: pip install ask-human-mcp + one line in .cursor/mcp.json → boom, you’re live - cross-platform: works on macOS, Linux, and Windows—no extra servers or webhooks. - markdown Q\&A: agent calls await ask_human(), question lands in ask_human.md with answer: PENDING. you write the answer, agent picks back up - file locking & rotation: prevents corrupt files, limits pending questions, auto-rotates when ask_human.md hits ~50 MB
the quickstart
pip install ask-human-mcp ask-human-mcp --help
add to .cursor/mcp.json and restart: { "mcpServers": { "ask-human": { "command": "ask-human-mcp" } } }
now any call like:
answer = await ask_human( "which auth endpoint do we use?", "building login form in auth.js" )
creates:
### Q8c4f1e2a ts: 2025-01-15 14:30 q: which auth endpoint do we use? ctx: building login form in auth.js answer: PENDING
just replace answer: PENDING with the real endpoint (e.g., `POST /api/v2/auth/login`) and your agent continues.
link:
github -> https://github.com/Masony817/ask-human-mcp
feedback:
I'm Mason a 19yo solo-founder at Kallro. Happy to hear any bugs, feature requests, or weird edge cases you uncover - drop a comment or open an issue! buy me a coffee -> coff.ee/masonyarbrough