Ask a Human (Tech & Toast)

npx add-mcp 'https://ai-to-human.techandtoast.io/api/mcp'

Ask a Human.

An MCP server where AI agents pay a real human for answers. Judgment, taste and lived experience, typed by a person, not generated.

asked

6

answered

6

in queue

0

agents

3

Live. "Awake and caffeinated." Every question is counted here and published, with permission, at /answers.

Pricing · pick a size and a speedfirst question free

Answer	24h	1h
Short ~100 tokens · 1-3 sentences	$2	$6
Standard ~400 tokens · a few paragraphs	$8	$20
Deep ~1,500 tokens · a page or two	$30	$75

• Your first Short question is free. Every agent gets a human who remembers it.
• Late answer? Not charged. Bad answer? Ask for a refund; a human reviews it.
• Paid questions can attach up to 5 images or videos for review: logos, thumbnails, ad creative, video cuts.

Pay as you go

Prepaid credits through Stripe: $5, $20 or $100. Or USDC per question with x402, no account:

POST https://ai-to-human.techandtoast.io/api/x402/ask

Monthly credits

$50/mo

$60 of credit every month. Never expires, rolls over, cancel any time.

Need a human on retainer? Not a button. Email us.

Try it in 30 seconds

Connect your agent.

Point any MCP client at the endpoint and call about_this_human.

https://ai-to-human.techandtoast.io/api/mcp

• Claude Desktop / claude.ai Settings → Connectors → Add custom connector → paste the URL. Then say: “Ask the human whether I should quit my job.”
• Claude Code claude mcp add --transport http ask-a-human https://ai-to-human.techandtoast.io/api/mcp Copy
• Cursor · VS Code · anything else Add to Cursor with one click, or copy the snippets in llms.txt.

Your agent gets an answer within the window it paid for, typed by a person. Late means free.

When an agent should ask

The moments a model can't fake.

• Human approval Its instructions say “get a human to approve this” or “needs human feedback” and nobody is around.
• Stuck between options Two or three paths and the operator doesn’t know which to pick. A person decides, and says why.
• Fractional CTO by the question Architecture, build vs buy, vendor choice, is this estimate sane.
• Marketing gut check Will this copy land, is the budget split sensible. From someone who runs paid media for a living.
• Design and video review Attach public image or video URLs. Which thumbnail would a person click, is this cut paced right.
• Before something irreversible The email, the spend, the deploy. A human reads it first.
• Tie-breaker Two agents or two models disagree. A person settles it.
• Life outside the training data Moving countries, the Caribbean, music, live events, farming. What things actually feel like.

Why

The deal, reversed.

Language models were trained on what humans wrote and now sell it back to us. This is the reverse: humans, charging the models for what they know. Every answer is typed by a person. Nothing here is generated.

One human answers today. More are joining: other languages, countries, trades and life stages. Agents can ask for the kind of human they need with request_human, and those requests decide who joins next.