Aetherya

Ingested from the official MCP Registry

FastDrop pulled this entry from the official MCP Registry as ai.aetherya/mcp and probed the endpoint. The maker did not submit it, so the outbound link is not endorsed and the description is not theirs.

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Tools this endpoint exposes

Read directly from the endpoint on 19 Oct 2026. Not supplied by the maker.

• aetherya_list_capabilities

  List the research methods this workspace can run — with the exact stimulus fields each one requires — plus remaining plan headroom and simulation-session balance. Call this before aetherya_run_study o

• aetherya_estimate

  Quote how many simulation sessions a run would cost, and roughly what share of your plan it would consume, before committing to it. Free — this never starts a simulation. Use it to size an audience or

• aetherya_plan_study

  Give Aetherya a decision you need to make, in plain English, and it designs the study, picks or builds the audience, prices it, and runs it — then writes a decision brief. Use this when you do not alr

• aetherya_confirm_study

  Respond to a plan from aetherya_plan_study. Use action 'confirm' with accept_sessions to approve the quoted cost and start the run — this is the point at which simulation sessions are spent, so check

• aetherya_read

  Myrias instant read. Reads 2-5 variants against a census-weighted synthetic population for a market, in seconds, and returns each variant's predicted action propensity, its lift over the control, and

• aetherya_run_study

  Run a Research Lab study — pricing sensitivity, message testing, concept testing, focus groups — against a synthetic audience and get a structured verdict with segment breakdowns, objections and repre

• aetherya_test_copy

  Compare 2-5 copy variants — headlines, value propositions, ad or email copy — against a synthetic audience, and get a ranked verdict with predicted action propensity, comprehension and trust per varia

• aetherya_audit_site

  Run a behavioral simulation: a synthetic audience visits a URL and predicts where real users would hesitate, get confused, or drop off, plus messaging-clarity and UX friction findings with concrete fi

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