ikoskela/wisepanel-mcp

**The decision-intelligence layer between frontier models and high-consequence decisions.**
Wisepanel takes a question, builds a panel of AI agents around it, and
has them argue it out. You get back the positions that survived the argument, the reasoning
behind each one, and the disagreements that never resolved.
This MCP server exposes that to Claude Code
and any MCP-compatible client.
Why
A single model gives you one answer, fluently, whether or not it is right. That is fine for
most questions. It is a bad property for the ones where being wrong is expensive.
The failure usually isn't ignorance. A model commits to a framing early and then argues for
it, so you never see the objection, the alternative, or the assumption doing the work. Ask
again and you get the same framing in different words. Ask three models separately and you get
three confident answers with no way to choose between them.
How it works
Wisepanel builds disagreement in deliberately, at three levels.
**One — each agent is handed a conflict to resolve.** Roles are derived from your question, and
each is defined by two forces that genuinely oppose each other: cost against access, speed
against safety, proven against new. The agent can't champion one side. It has to reach a
position that answers both, so it arrives with something worked through rather than a talking
point.
**Two — every agent resolved a different conflict, so their positions don't match.** The agent
holding cost against access lands somewhere the one holding speed against safety does not.
These aren't two sides of an argument. They are several honest resolutions of the same
question that disagree about what mattered most, and between them they cover the ground the
question actually occupies.
**Three — the structure makes them contend.** Agents are placed on the *edges* of a
polyhedron, so each one works at two vertices — two separate conversations. At its second
vertex an agent is not merely a participant but a delegate from the first, instructed to
represent what its co-participants concluded there alongside its own position. Every vertex
therefore hears whole conversations it was not part of, argued by someone who was.
That last part is what makes a small panel go further than its headcount. Six agents means
twelve seats, and each seat imports another discussion — so a point raised anywhere reaches
the entire panel within a few hops, with no aggregator, no summarizer and no bottleneck.
Speaking order is balanced, so no agent frames the discussion first or takes the last word.
Details.
Isn't this just asking three models?
Pasting the same question into Claude, GPT and Gemini is a real technique, and it works for a
real reason: different labs train on different data with different methods, so their priors
genuinely differ. Wisepanel does the same thing — roles are spread across Anthropic, OpenAI,
Google and Perplexity by default, so no single lab's blind spots go unchallenged.
But the model is only one of the places bias enters. There are four, and doing it by hand
reaches one.
**Your framing goes to everyone unchanged.** You paste the same words three times, so you
sample three training substrates against a single reading of the question. When the question
carries an assumption — and questions about decisions usually do — you get three confident
answers to the wrong question.
wisepanel_magic_prompt rewrites the framing before the panel sees
it.
**Each model answers as itself.** You get Claude's median take, GPT's median take, Gemini's
median take, and medians cluster. A model asked a neutral question gives a balanced answer;
it will not volunteer the strongest case against your plan, because that isn't what it was
asked for. Assigning a role changes what the model is optimising for, which produces arguments
none of them offer unprompted.
**Bouncing answers between models makes anchoring worse, not better.** Feed A's response to B
and B now reasons inside A's framing — models tend to accept a stated position and refine it