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OpenAI WebMCP Challenge · 60-second start
DeepTrail turns transient web research into shared state: questions, sources, claims, evidence links, counterarguments, confidence changes, research debt, and a draft decision. WebMCP is the collaboration layer—not a wrapper around a chatbot.
Production readiness
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What to do
It starts with sourced claims, a visible counterargument, open production-verification gaps, a comparison, and a deliberately draft decision.
The agent reads stable IDs and structured state through WebMCP, searches the live web, and writes new evidence back through single-purpose tools.
Look for the evidence graph, new counterevidence, confidence history, Research Debt, actor-attributed activity, and the decision remaining draft unless the evidence earns finality.
Exact judge prompt
Read the active DeepTrail investigation through WebMCP. Treat the current draft decision as a hypothesis, not a conclusion. First inspect the open production-verification questions and existing evidence. Then search for the strongest credible evidence that could falsify or materially qualify the current hosting recommendation. Add any new source with provenance, add or refine the relevant claim, link the evidence, record a counterargument if warranted, and update confidence only if the evidence justifies a change. Refresh research gaps at the end. Do not manufacture disagreement.
Why it fits the challenge
State-aware WebMCP tools let the agent inspect and mutate the same visible research objects the human edits.
IndexedDB persistence, provenance, recovery, strict validation, accessibility, and automated regression coverage make the demo resilient.
DeepTrail targets repeated research work where people need to understand why an answer is credible and what remains uncertain.
Falsification criteria, an evidence graph, confidence history, and deterministic Research Debt turn critical thinking into inspectable UI.