Post-launch feedback, the AI-native way.

Open Typeform or Google Forms. Rebuild a post-launch survey from scratch — NPS, some open text, maybe a feature ranking grid. Duplicate in Canny or Intercom Surveys if you want product-managed voting. Drop the link in the welcome email. Follow up three days later with a reminder. Export the CSV. Skim 140 rows of feedback. Open a doc. Try to write a coherent synthesis. Triage feature requests by hand. Manually update the top five into your Linear or GitHub roadmap.

Every step of that pipeline assumes a human is doing the reading. But if you already have Claude or Cursor open while you're building your product — and you almost certainly do — the shape of the right tool changes. The agent can design the schema, own the loop, and hand you a grounded synthesis. The hosted form becomes an implementation detail.

HumanSurvey is a thin, opinionated survey API plus an MCP server. Your agent already has context — it knows your product, your positioning, and the questions that are actually open for you right now. You tell it what you want to learn. It does the rest:

1. 01Designs the schema from your intent — NPS, choice questions with options from your positioning hypotheses, scales for pricing signal, open text for qualitative.
2. 02Creates the survey, returns /s/{id}. You paste into the welcome email, Discord, or Slack. Or ask your agent to post if it has an email/channel tool.
3. 03On demand, retrieves results and summarizes. "What did our first users say?" becomes a structured answer with percentages, themes, and suggested moves — all grounded in actual response JSON.

Nothing in this loop requires you to leave your agent conversation. No tab-switching, no export-to-CSV, no “I'll read the responses this weekend” that turns into never.

You launched a new analytics tool for e-commerce stores. 240 people signed up in the first 48 hours. You want to learn: is the positioning landing, what's the biggest friction, are people willing to pay at your intended price? You open Claude Code with HumanSurvey installed and say:

“Survey our first 200 sign-ups. I want: NPS 0–10; why they signed up (the positioning options: replacing their current analytics / integrating with Shopify / because of our pricing / the AI-summary feature); biggest paper cut so far as open text; would they pay at $29/month (yes / maybe / no). Keep it 2 minutes.”

Claude generates a schema roughly like:

{
  "title": "How's it going so far?",
  "description": "Two minutes. Helps us prioritize what to build next.",
  "sections": [{
    "questions": [
      { "type": "scale", "label": "How likely are you to recommend us to a friend?",
        "min": 0, "max": 10,
        "minLabel": "Not at all", "maxLabel": "Definitely" },
      { "type": "multi_choice", "label": "Why did you sign up?",
        "options": [
          { "label": "Replacing my current analytics tool" },
          { "label": "Native Shopify integration" },
          { "label": "The pricing is better than alternatives" },
          { "label": "The AI summary feature" },
          { "label": "Other" }
        ]
      },
      { "type": "text", "label": "Biggest paper cut so far?" },
      { "type": "single_choice", "label": "Would you pay $29 / month for this?",
        "options": [
          { "label": "Yes" }, { "label": "Maybe" }, { "label": "No" }
        ]
      }
    ]
  }],
  "max_responses": 240,
  "expires_at": "2026-04-27T23:59:00Z"
}

Claude calls create_survey, returns https://www.humansurvey.co/s/lnc789. You drop it in the welcome email Resend automation. A week later, you ask:

“What did our first users say?”

Claude calls get_results and returns:

112 responses (56%). NPS: 42.
  — 38% promoters, 44% passives, 18% detractors.
  — Detractor theme (open text): slow initial data sync.

Why they signed up:
  1. Native Shopify integration (63%) — clear lead.
  2. AI summary feature (51%).
  3. Replacing current analytics (28%) ← the positioning we lead with.
  4. Pricing (22%).

Biggest paper cut (top themes from 94 open-text responses):
  1. Google SSO missing (41 mentions) — by far the loudest.
  2. Initial data sync is slow (18 mentions) — matches detractor NPS cluster.
  3. No way to invite teammates yet (12 mentions).

Pricing at $29/month: 67% yes, 24% maybe, 9% no.
At $39/month (follow-up segment): 48% yes. Demand curve
  suggests $29 is close to the right anchor.

Suggested moves:
  1. Rewrite the hero: lead with Shopify integration, not
     "replacing your analytics tool".
  2. Ship Google SSO this week — it's the #1 loudest complaint.
  3. Diagnose the initial sync latency — it's hitting both the
     paper-cut list and the detractor segment.
  4. Anchor pricing at $29; test $35 in a later cohort.

Every number and theme in that synthesis is traceable back to the raw JSON. You didn't read 112 responses by hand. You didn't triage by gut feel. And you got a concrete list of four actions you can execute before lunch.

• Waitlist warm-up survey. Before you ship, ask your waitlist which features they'd use first, what they're currently paying elsewhere, and what would unlock them switching. Free, early positioning data.
• Private-beta weekly pulse. Every Friday during beta, send beta users a 3-question check — what they tried this week, what broke, what would they ship next. Your agent stitches the weekly results into a running changelog-driven narrative.
• Pricing validation (van Westendorp style). Four scale questions on price sensitivity. Your agent computes the optimal price range and tells you where to anchor.
• Churn / cancellation exit survey. Triggered when a user downgrades. Structured reasons + free text. Agent surfaces the top-three recurring reasons monthly and flags individual responses worth replying to.
• Rolling NPS on a cohort basis. One survey per monthly sign-up cohort with an expires_at of +30 days. Your agent graphs NPS by cohort and tells you if recent product changes moved the number.

Post-launch feedback tools fall into three rough buckets. Pick the one that matches how your feedback will be consumed:

If you're going to read every response yourself, Typeform has a nicer reading experience. If you want users to post and vote on feature requests as a running list, Canny is a different shape of product. HumanSurvey is the right pick when your agent is already in the loop and you want the synthesis to flow back into its context — not a dashboard you'll open once a week.

1. 01Add HumanSurvey as an MCP server in Claude Code (~/.claude.json). Full config snippet in the docs.
2. 02Ask Claude: "create an API key for HumanSurvey." It calls create_key. Next time you want to survey your users, just describe what you want to learn.