@waldzellai/structured-argumentation

Record a sequential thought from the model. Parameters mirror the popular sequential thinking MCP server so models can revise or branch their reasoning when needed.

A detailed tool for applying formal scientific reasoning to questions and problems. This tool guides models through the scientific method with structured hypothesis testing. It enforces explicit variable identification, prediction making, and evidence evaluation. When to use this tool: - Investigating cause-effect relationships - Evaluating competing explanations for phenomena - Developing and testing hypotheses - Avoiding confirmation bias in reasoning - Conducting systematic inquiry Key features: - Structured scientific process - Explicit variable identification - Controlled experimental design - Systematic evidence evaluation - Iterative hypothesis refinement

A specialized tool for conducting LLM-based focus groups to evaluate MCP servers. This tool helps models analyze MCP servers from multiple user perspectives. It provides a framework for structured evaluation, feedback collection, and recommendation generation. When to use this tool: - When evaluating a new or updated MCP server - To identify usability issues from different LLM user perspectives - To gather diverse feedback on API design and functionality - To prioritize improvements based on user needs - When seeking to understand how different types of users might interact with your MCP server Key features: - Multi-persona simulation of different LLM users - Structured feedback collection process - Focus area analysis for targeted improvements - Synthesis of findings across user types - Actionable recommendation generation

A detailed tool for systematic self-monitoring of knowledge and reasoning quality. This tool helps models track knowledge boundaries, claim certainty, and reasoning biases. It provides a framework for metacognitive assessment across various domains and reasoning tasks. When to use this tool: - Uncertain domains requiring calibrated confidence - Complex reasoning chains with potential biases - Technical domains with clear knowledge boundaries - Scenarios requiring distinction between facts, inferences, and speculation - Claims requiring clear evidential basis Key features: - Knowledge boundary tracking - Claim classification and confidence calibration - Reasoning quality monitoring - Bias and assumption detection - Uncertainty area identification

A detailed tool for systematic dialectical reasoning and argument analysis. This tool helps analyze complex questions through formal argumentation structures. It facilitates the creation, critique, and synthesis of competing arguments. When to use this tool: - Evaluating competing perspectives and claims - Analyzing complex ethical dilemmas - Assessing policy proposals with multiple stakeholders - Exploring scientific hypotheses and counter-arguments Key features: - Break down arguments into claims, premises, and conclusions - Track relationships between arguments - Represent objections and rebuttals - Facilitate dialectical progression through thesis-antithesis-synthesis - Evaluate argument strengths and weaknesses - Visualize argument structures Parameters explained: - claim: The central proposition being argued - premises: Supporting evidence or assumptions - conclusion: The logical consequence of accepting the claim - argumentType: Whether this is a thesis, antithesis, synthesis, objection, or rebuttal - confidence: Your confidence level in this argument (0.0-1.0) - respondsTo: ID of an argument this directly responds to - supports/contradicts: IDs of arguments this supports or contradicts - strengths/weaknesses: Notable strong or weak points of the argument - nextArgumentNeeded: Whether another argument is needed in the dialectic

A detailed tool for diagrammatic thinking and spatial representation. This tool helps models create and manipulate visual representations of problems. It can be used to develop system diagrams, flowcharts, concept maps, and other visual models. When to use this tool: - System architecture design - Algorithm visualization - Concept mapping and knowledge organization - Pattern recognition in complex data - Spatial problem solving Key features: - Create and manipulate abstract visual elements - Support for multiple visual representation types - Track iterations of visual thinking - Translate between verbal descriptions and visual representations - Generate visual insights from patterns Parameters explained: - operation: The type of action to perform (create, update, delete, transform, observe) - elements: The visual elements to operate on - diagramId: Identifier for the diagram being created or modified - diagramType: The type of diagram (graph, flowchart, state diagram, etc.) - iteration: The current iteration of the visual reasoning process - observation/insight/hypothesis: Verbal reasoning about the visual representation - nextOperationNeeded: Whether another operation is needed in the visual reasoning process

Evaluate a proposed action using multiple ethical frameworks.

A detailed tool for simulating expert collaboration with diverse perspectives. This tool helps models tackle complex problems by coordinating multiple viewpoints. It provides a framework for structured collaborative reasoning and perspective integration. When to use this tool: - Complex problems requiring diverse expertise - Issues needing multiple stakeholder perspectives - Scenarios with potential value trade-offs - Creative tasks benefiting from diverse viewpoints - Problems where biases might limit single-perspective thinking Key features: - Multi-persona simulation - Structured collaboration process - Productive disagreement management - Cross-pollination of ideas - Perspective synthesis and integration