LABSCO SUMMARY
Install the one that matches the thinking you need, not the whole collection.
These add no external data — the value is the scaffold, which forces a model to externalise steps it would otherwise skip: state the counter-argument, name the hypothesis, score the option against the criterion, mark its own confidence. Because each is a separate package, the sensible pattern is to pick the one or two that fit the work in front of you; loading thirteen reasoning frames into one client mostly costs context.
What it is
A monorepo of separate MCP servers, each one imposing a named reasoning structure on a problem rather than fetching data from anywhere.
What you get
- Structured Argumentation — formal dialectical reasoning, claims and counter-claims held apart
- Scientific Method — hypothesis testing and evidence evaluation
- Decision Framework — structured decision analysis
- Collaborative Reasoning — the same problem worked from several perspectives
- Metacognitive Monitoring — knowledge assessment and confidence tracking
- Bias Detection — flagging potentially biased wording
- Constraint Solver — validating logical and numeric constraints
- Analogical Reasoning, Visual Reasoning, Ethical Reasoning, Narrative Planner, Goal Tracker and Multimodal Synthesizer round out the set
Requirements
Nothing to authorize — no account, no key, no network calls to a vendor. Each server installs on its own; @waldzellai/structured-argumentation is the documented example, run with npx, and every server is also published as a Docker image.
Setup effort
One command — npm install @waldzellai/structured-argumentation
USE CASES
01
Pressure-testing a proposal before you send it
Work it as a formal argument so the counter-claims are written down instead of quietly skipped.
02
Choosing between options that are close
Put them through a structured decision analysis rather than a list of pros and cons that hides the weighting.
03
Checking whether a piece of writing leans
Run it through bias detection to see the wording that carries a slant.
04
Knowing when the model is guessing
Metacognitive monitoring makes confidence an explicit output rather than a tone of voice.
BEFORE YOU INSTALL
What to know first
Requires
npx on PATH
Where it runs
The documented client runs the server on your machine.
INSTALLATION
Connect your client
Server setup
TERMINAL
npm install @waldzellai/structured-argumentationMCP Clients
Claude Desktop
CLAUDE DESKTOP · JSON
{
"mcpServers": {
"structured-argumentation": {
"command": "npx",
"args": [
"-y",
"@waldzellai/structured-argumentation"
]
}
}
}