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Run conversational surveys with skip logic, resume support, and flexible storage.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "@cyanheads/survey-mcp-server" yet — see the docs or source repo.
Start a conversational screening survey for user interviews with four questions: role, industry, whether they have used collaboration software, and team size. If they have not used collaboration software, skip the deep usage questions. Support session resume and save results to a queryable store.
A conversational screening survey flow with skip logic, resume support, and structured stored results.
Create a conversational event satisfaction survey. First ask attendee type and rating. If the rating is below 3, ask follow-up questions about the most unsatisfactory part and improvement suggestions. If the rating is 4 or higher, ask about favorite content and likelihood to recommend. Allow respondents to resume later.
A dynamically branching satisfaction survey that follows up automatically and supports session resumption.
Collect feedback on a new feature through conversation. First confirm whether the user has used the feature. If yes, ask about usage frequency, pain points, and the most desired new capability. If not, ask why they have not used it. Save each user's answers by session ID for later analysis.
A conversational feature feedback survey with conditional branching and session-based result storage.
Create surveys and validate questions with AI for agents.
Analyze papers locally, map citations, and surface verified contradictions.
Run a universal MCP server with AI memory and semantic search.
Control macOS settings, windows, audio, displays, and Focus mode via MCP.
Provide AI agents with developer utilities like code review, JSON formatting, and password generation.
Store, search, and create YAML workflow playbooks for LLM agents.