humansurvey-mcp
Feedback collection for AI agents. Create surveys from JSON schema, collect responses from groups of people, and retrieve structured results.
README
HumanSurvey
Feedback collection infrastructure for AI agents.
HumanSurvey lets an agent doing long-horizon work collect structured feedback from a group of people:
Agent is doing a job
→ needs structured feedback from a group
→ creates survey from JSON schema
→ shares /s/{id} URL with respondents
→ humans respond over hours or days
→ agent retrieves structured JSON results and acts on them
What is this?
HumanSurvey is a minimal API and MCP server for one narrow job: let agents collect structured feedback from groups of humans and get machine-usable results back.
It is designed for:
- AI agents running event management, product launches, or community workflows that need to survey a group
- Developers building agent products that need a lightweight feedback-collection primitive
It is not designed for:
- survey dashboards
- visual form builders
- template libraries
- email campaigns
- analytics/reporting UI
Features
- JSON schema input — structured, precise, and directly machine-generated
- MCP server — create surveys and read results directly from Claude Code
- Minimal API surface — authenticated creator routes, public respondent submission
- Four semantic question types —
choice,text,scale,matrix - Conditional logic —
showIfin Markdown and JSON schema - Explicit lifecycle — close surveys, expiry, and max response limits
Product Principles
- Semantic over visual: HumanSurvey has a small protocol, not a zoo of UI-specific field types.
- AI-first I/O: agents write the survey and agents consume the results; humans are in the middle.
- Everything is an API: creator functionality must be available over authenticated HTTP and MCP.
- Narrow scope wins: if a feature mainly serves human survey operators, it probably does not belong here.
Supported Question Types
single_choicemulti_choicetextscalematrix
Markdown Syntax
# Survey Title
**Description:** Instructions for the respondent.
## Section Name
**Q1. Your question here?**
- ☐ Option A
- ☐ Option B
- ☐ Option C
**Q2. Multi-select question?** (select all that apply)
- ☐ Choice 1
- ☐ Choice 2
- ☐ Choice 3
**Q3. Open-ended question:**
> _______________
| # | Item | Rating |
|---|------|--------|
| 1 | Item A | ☐Good ☐OK ☐Bad |
| 2 | Item B | ☐Good ☐OK ☐Bad |
Scale questions:
**Q4. How severe is this issue?**
[scale 1-5 min-label="Low" max-label="Critical"]
Conditional logic:
**Q1. Did the deploy fail?**
- ☐ Yes
- ☐ No
**Q2. Which step failed?**
> show if: Q1 = "Yes"
> _______________________________________________
Quick Start
Use with Claude Code
Add to your Claude Code config (~/.claude.json):
{
"mcpServers": {
"survey": {
"command": "npx",
"args": ["-y", "humansurvey-mcp"],
"env": {
"HUMANSURVEY_API_KEY": "hs_sk_your_key_here"
}
}
}
}
Then in Claude Code:
> Create a post-event feedback survey with a 1-5 rating, open text, and a yes/no question
Available tools:
create_key— self-provision an API key; no human setup requiredcreate_survey— create from JSON schema; optionalmax_responses,expires_at,webhook_urlget_results— aggregated results + raw responseslist_surveys— list surveys owned by your keyclose_survey— close a survey immediately
Use the HTTP API
curl -X POST https://www.humansurvey.co/api/keys \
-H "Content-Type: application/json" \
-d '{
"name": "my claude agent",
"email": "you@example.com",
"wallet_address": "eip155:8453:0xabc..."
}'
All fields optional. wallet_address uses CAIP-10 format — will be used for agent-native payments in the future.
Then create a survey:
curl -X POST https://www.humansurvey.co/api/surveys \
-H "Authorization: Bearer hs_sk_..." \
-H "Content-Type: application/json" \
-d '{
"schema": {
"title": "Post-Event Feedback",
"sections": [{
"questions": [
{ "type": "scale", "label": "How would you rate the event?", "min": 1, "max": 5 },
{ "type": "text", "label": "What should we improve?" }
]
}]
}
}'
Response:
{
"survey_url": "/s/abc123",
"question_count": 1
}
Read results:
curl https://www.humansurvey.co/api/surveys/abc123/responses \
-H "Authorization: Bearer hs_sk_..."
Public Surface
- Docs page:
https://www.humansurvey.co/docs - OpenAPI:
https://www.humansurvey.co/api/openapi.json - AI index:
https://www.humansurvey.co/llms.txt
Tech Stack
| Component | Technology |
|---|---|
| Framework | Next.js (App Router) |
| Database | Neon (serverless Postgres) |
| Parser | remark (unified ecosystem) |
| Frontend | React + Tailwind CSS |
| MCP Server | @modelcontextprotocol/sdk |
| Deployment | Vercel |
Project Structure
├── apps/web/ # Next.js app (API + frontend)
├── packages/parser/ # Markdown → Survey JSON parser
├── packages/mcp-server/ # MCP server for Claude Code
└── docs/ # Architecture docs
Contributing
Read CONTRIBUTING.md before opening a PR. The most important rule is scope discipline: new UI variants, analytics dashboards, and human-operator features are usually out of scope.
Development
pnpm install
pnpm dev # Start Next.js dev server
pnpm --filter @mts/parser test
pnpm build # Build all packages
License
MIT
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.