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cognitive_reasoning

Process complex problems through multi-modal reasoning, collaborative AI personas, and dynamic knowledge graphs. Supports analytical, creative, and critical thinking modes to generate well-reasoned solutions.

Instructions

Advanced cognitive processing tool with multi-modal reasoning, knowledge graphs, and collaborative thinking.

This revolutionary tool implements an advanced cognitive architecture that goes far beyond simple sequential thinking:

MULTI-MODAL REASONING MODES: • analytical: Systematic, logical, step-by-step analysis • creative: Divergent thinking and novel connection generation
• intuitive: Pattern recognition and holistic insights • critical: Rigorous evaluation and skeptical analysis • synthetic: Integration across multiple perspectives

COLLABORATIVE PERSONAS: • analyst: Data-driven, methodical, evidence-based reasoning • creator: Innovative, imaginative, breakthrough thinking • critic: Quality control, bias detection, rigorous evaluation • synthesizer: Integration, consensus building, holistic understanding

DYNAMIC KNOWLEDGE GRAPH: • Automatic concept extraction and relationship mapping • Real-time activation spreading and relevance scoring • Context-aware clustering and pattern detection • Visual representation of thought networks

META-COGNITIVE REFLECTION: • Self-assessment of thinking quality and effectiveness • Strategy adaptation based on problem complexity • Confidence calibration and uncertainty quantification • Learning from past reasoning patterns

ADVANCED FEATURES: • Parallel processing streams for complex problems • Adaptive complexity management • Real-time quality metrics and assessment • Context-aware knowledge activation • Visual thought mapping and relationship diagrams • Collaborative consensus building • Learning memory system

When to use this tool:

  • Complex multi-faceted problems requiring diverse perspectives

  • Creative ideation with systematic evaluation

  • Problems where uncertainty and confidence matter

  • Learning and knowledge synthesis tasks

  • Strategic planning with multiple considerations

  • Research and investigation workflows

  • Any situation requiring high-quality, well-reasoned thinking

The tool automatically selects optimal reasoning modes and personas based on the input, but you can also specify them explicitly for targeted thinking approaches.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
thoughtYesThe thought or problem to process through the cognitive engine
reasoning_modeNoSpecific reasoning mode to use (auto-selected if not specified)
meta_reflectionNoWhether to include meta-cognitive reflection (default: true)
personas_activeNoActive thinking personas (auto-selected based on complexity if not specified)
session_contextNoOptional context from previous thoughts in the session
complexity_levelNoProblem complexity level 1-10 (auto-assessed if not specified)
parallel_streamsNoNumber of parallel processing streams (default: matches persona count)
knowledge_mappingNoWhether to update knowledge graph and show visual map (default: true)
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It details multi-modal reasoning, collaborative personas, dynamic knowledge graph, meta-cognitive reflection, and advanced features like parallel processing. However, it omits limitations, error behavior, response format, and cost implications, leaving some gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is excessively long with bullet points and sections. While it is front-loaded with the main purpose, the extensive detail on each mode and feature could be condensed. The structure is clear but not concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity and 8 parameters, the description covers usage, modes, and features well. However, there is no output schema, and the description does not specify what the tool returns or how responses are structured, leaving a completeness gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for all 8 parameters. The description adds value by explaining each reasoning mode, persona, meta_reflection, and other parameters in narrative form, providing context beyond schema definitions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it is an advanced cognitive processing tool for multi-modal reasoning, knowledge graphs, and collaborative thinking. It lists specific reasoning modes and personas, but the purpose is somewhat buried in verbose marketing language, reducing clarity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly provides a 'When to use this tool' section listing complex multi-faceted problems, creative ideation, strategic planning, etc. It also mentions automatic selection of modes and personas. However, no sibling tools exist for comparison, so alternative guidance is not applicable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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