This server provides structured cognitive processing tools based on traditional Anishinaabe knowledge (Gikendaasowin) and modern reasoning techniques:
assess_and_orient
: Mandatory initial assessment using the CUC-N (Complexity, Uncertainty, Consequence, Novelty) framework to perform the 'Observe' and 'Orient' steps of the OOReDAct cycle.think
: Executes the core Observe-Orient-Reason-Decide-Act (OOReDAct) deliberation cycle, adapting reasoning styles (Chain-of-Thought, Chain-of-Draft/Condensed Reasoning, Structured Chain-of-Thought) for robust decision-making.mental_sandbox
: Logs internal cognitive simulations (Hypothesis Testing, Constraint Checks, Confidence Scoring, Pre-computational Analysis) before executing non-trivial actions.quick_think
: Provides minimal cognitive acknowledgement for simple outcomes not requiring full deliberation.
Enables support for the project through the Buy Me a Coffee platform, providing an alternative donation method with a direct button integration.
References GitHub as the repository host with link integration, including links for accessing project resources and documentation.
Ensures all responses are formatted in standard Markdown, with specific support for code blocks, inline code, and mathematical notation.
Provides packages published on npm registry under two parallel names (@nbiish/gikendaasowin-aabajichiganan-mcp and @nbiish/cognitive-tools-mcp) with identical functionality.
Provides donation functionality via Stripe, allowing users to financially support the project through the included QR code and link.
◈──◆──◇ GIKENDAASOWIN AABAJICHIGANAN MCP SERVER / ENHANCED 6-STAGE COGNITIVE TOOLS MCP SERVER ◇──◆──◈
Revolutionary MCP server with Enhanced 2-Round, 6-Stage Cognitive Deliberation Framework (v10.0.2). The deliberate
tool implements an LLM-guided cognitive enhancement framework that prompts LLMs to evaluate and select from 15 modern cognitive techniques using a 0.00-0.99 scoring system with ≥1.53 threshold rule - accepting only input
and context
parameters. (Integration guidelines in latest.md
are licensed under LICENSE.)
🚀 MAJOR UPDATE (v10.0.2): Fixed critical variable reference bug that was causing "ReferenceError: prompt is not defined" when using the deliberation tool. The tool now properly references the input
parameter throughout the framework, ensuring reliable operation for all LLM implementations without input restrictions.
Known as:
- Anishinaabemowin:
@nbiish/gikendaasowin-aabajichiganan-mcp
- English:
@nbiish/cognitive-tools-mcp
Both packages are maintained in parallel and receive the same updates. You can use either package name in your projects - they provide identical functionality.
See the latest integration details in latest.md
.
ᐴ REPOSITORY STRUCTURE ᔔ [MAZINAAZIWIN] ◈──◆──◇──◆──◈
Current repository structure (v10.0.2):
ᐴ GASHKITOONAN ᔔ [ENHANCED CAPABILITIES] ◈──◆──◇──◆──◈
🚀 Revolutionary LLM-Guided Cognitive Framework (v10.0.2)
FIXED IN v10.0.2: CRITICAL VARIABLE REFERENCE BUG
- Critical Bug Fix: Resolved "ReferenceError: prompt is not defined" error that prevented LLM usage
- Proper Parameter Usage: Tool now correctly references the
input
parameter throughout the deliberation framework - Enhanced Reliability: No more runtime errors when LLMs call the deliberation tool
- Unrestricted Usage: LLMs can now use this MCP tool without any input restrictions or failures
Core Framework Features (Maintained from v8.9.6):
- LLM-Guided Evaluation: Tool prompts LLMs to evaluate cognitive techniques rather than providing hardcoded scores
- True Adaptive Reasoning: No pre-calculated evaluations - LLMs determine optimal strategies dynamically
- 15 Modern Cognitive Techniques: Complete prompting strategy arsenal from Cache-Augmented Reasoning to Multimodal Synthesis
- 0.00-0.99 Scoring Framework: LLMs evaluate solution level + efficiency level with ≥1.53 threshold rule
- Iterative Enhancement: Built-in guidance to return to deliberate after tool usage for continuous improvement
Revolutionary LLM-Guidance Architecture:
Framework Delivery Phase:
- 6-Stage Structure Presentation - Complete deliberation framework with critical thinking questions
- Cognitive Technique Catalog - All 15 modern prompting strategies for LLM evaluation
- Scoring Instructions - Clear 0.00-0.99 evaluation criteria with threshold-based selection rules
LLM Processing Phase:
- Dynamic Technique Selection - LLM evaluates and scores techniques based on specific problem context
- Strategy Combination - Multiple techniques used when scoring ≥1.53 for enhanced effectiveness
- Tool Planning Integration - Recommendations for tool usage count and re-deliberation timing
- Adaptive Implementation - LLM applies selected cognitive approaches to actual problem solving
Core Innovation: This breakthrough enables LLMs to meta-cognitively select their own reasoning approaches rather than following pre-determined paths, resulting in truly adaptive and context-appropriate problem solving.
ᐴ APITENDAAGOZIJIG ᔔ [PERFORMANCE METRICS] ◈──◆──◇──◆──◈
- LLM-Guided Evaluation: Prompts LLMs to evaluate and score 15+ cognitive techniques dynamically
- True Adaptive Reasoning: No hardcoded evaluations - strategies selected based on problem context
- Meta-Cognitive Enhancement: LLMs learn to select their own optimal reasoning approaches
- Iterative Improvement: Built-in guidance for tool usage and re-deliberation cycles
- Threshold-Based Selection: ≥1.53 scoring rule ensures quality technique combination
🚀 LLM-Guided Cognitive Enhancement Framework (v8.9.6)
This revolutionary update transforms the deliberation tool from a hardcoded evaluation system into an LLM-guided cognitive enhancement framework that enables true adaptive reasoning.
Core Innovation
Meta-Cognitive Selection: The tool now prompts LLMs to evaluate and select cognitive techniques themselves using a structured 0.00-0.99 scoring system, enabling context-appropriate strategy selection rather than pre-determined approaches.
Processing Architecture
- Framework Presentation: Delivers complete 6-stage deliberation structure with critical thinking questions
- Technique Catalog: Presents all 15 modern cognitive techniques for LLM evaluation
- Scoring Framework: Guides LLM through solution level + efficiency level evaluation (≥1.53 threshold)
- Dynamic Selection: LLM chooses optimal techniques based on specific problem requirements
- Implementation Guidance: Provides tool usage recommendations and re-deliberation encouragement
This breakthrough enables LLMs to learn and adapt their own reasoning approaches while leveraging advanced cognitive frameworks for enhanced problem-solving capabilities!
ᐴ OZHITOON ᔔ [BUILDING] ◈──◆──◇──◆──◈
ᐴ AABAJICHIGAN ᔔ [USAGE] ◈──◆──◇──◆──◈
Claude Desktop Integration
Add to your claude_desktop_config.json
:
Available Tools
deliberate
- Advanced 2-Round, 6-Stage Cognitive Processing Engine (v8.9.2)
- Input Parameters:
input
(required): The problem, question, or situation needing cognitive deliberationcontext
(optional): Additional background information or constraints
- Auto-Processing: Automatically determines optimal cognitive approach and selects best prompting strategies
- Output: Pure cognitive processing results with natural tool usage count integration
- Features: Dynamic strategy evaluation, 2-round processing architecture, zero filler verbiage approach
ᐴ MIŻIWEWIN ᔔ [CITATION/SHARING] ◈──◆──◇──◆──◈
Please cite this project using the following BibTeX entry:
This project is licensed under the COMPREHENSIVE RESTRICTED USE LICENSE FOR INDIGENOUS CREATIONS WITH TRIBAL SOVEREIGNTY, DATA SOVEREIGNTY, AND WEALTH RECLAMATION PROTECTIONS and follows CONTRIBUTING.md guidelines.
Copyright © 2025 ᓂᐲᔥ ᐙᐸᓂᒥᑮ-ᑭᓇᐙᐸᑭᓯ (Nbiish Waabanimikii-Kinawaabakizi), also known legally as JUSTIN PAUL KENWABIKISE, professionally documented as Nbiish-Justin Paul Kenwabikise, Anishinaabek Dodem (Anishinaabe Clan): Animikii (Thunder), a descendant of Chief ᑭᓇᐙᐸᑭᓯ (Kinwaabakizi) of the Beaver Island Band, and an enrolled member of the sovereign Grand Traverse Band of Ottawa and Chippewa Indians. This work embodies Traditional Knowledge and Traditional Cultural Expressions. All rights reserved.
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