COAIA Sequential Thinking

[](https://mseep.ai/app/arben-adm-mcp-sequential-thinking) # MCP Server: Creative Orientation Engine ## STC * SEE: [STCREFACTORING.md](STCREFACTORING.md) ## Overview The MCP Server: Creative Orientation Engine is a groundbreaking package designed to facilitate advanced outcome creation through sequential, structural thinking. By fundamentally shifting the orientation from problem-solving to a creative focus, this engine empowers users to envision and manifest desired futures. [](https://www.python.org/downloads/) [](https://opensource.org/licenses/MIT) [](https://github.com/psf/black) <a href="https://glama.ai/mcp/servers/m83dfy8feg"><img width="380" height="200" src="https://glama.ai/mcp/servers/m83dfy8feg/badge" alt="COAIA Sequential Thinking Server MCP server" /></a> ## Key Features - **Structural Tension Analysis**: The engine analyzes the gap between a clearly defined desired outcome and the current reality, identifying the inherent tension that drives progress. - **Creative Orientation**: By prioritizing the creation of new possibilities and focusing on desired outcomes, the engine guides users away from reactive problem elimination and towards proactive creation. - **Sequential Structuring**: The engine facilitates a structured approach to achieving outcomes, breaking down the journey into logical, advancement-driving steps. ## Benefits - **Enhanced Outcome Creation**: By adopting a creative orientation and focusing on structural tension, users develop more effective strategies for manifesting desired futures. - **Increased Generative Capacity**: The sequential structuring approach enables users to systematically build towards their desired outcomes, fostering innovation and progress. - **Cultivated Creativity**: By emphasizing the creation of new possibilities and the resolution of structural tension, the engine cultivates an environment that promotes generative thinking. ## Applications - **Strategic Visioning**: Ideal for organizations seeking to define and realize ambitious future states. - **Personal Development**: Individuals can leverage the engine to clarify and achieve personal aspirations through a structured, outcome-focused process. - **Innovation and Design**: A valuable tool for fostering innovation by guiding the creation of novel solutions and experiences. ## Technical Specifications - **Engine Architecture**: Built on a robust architecture ensuring high performance and reliability in driving creative processes. - **User Interface**: Designed for intuitive navigation, enabling users to easily engage with the engine's outcome-creation functionalities. - **Integration Capabilities**: Seamlessly integrates with other systems to support comprehensive creative workflow management. ## Conclusion The MCP Server: Creative Orientation Engine marks a significant advancement in technology for outcome creation. By embedding a creative orientation and a focus on structural tension, this engine empowers users to move beyond reactive problem-solving and actively shape their desired futures. ## References Fritz, R. (1999). The path of least resistance: Learning to become totally immersed in the creative process. Fawcett Columbine. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-292. Russell, S. J., & Norvig, P. (2003). Artificial intelligence: A modern approach. Prentice Hall. ## Prerequisites - Python 3.10 or higher - UV package manager ([Install Guide](https://github.com/astral-sh/uv)) ## Key Technologies - **Pydantic**: For data validation and serialization - **Portalocker**: For thread-safe file access - **FastMCP**: For Model Context Protocol integration - **Rich**: For enhanced console output - **PyYAML**: For configuration management ## Project Structure ``` mcp-sequential-thinking/ ├── mcp_coaia_sequential_thinking/ │ ├── server.py # Main server implementation and MCP tools │ ├── models.py # Data models with Pydantic validation │ ├── storage.py # Thread-safe persistence layer │ ├── storage_utils.py # Shared utilities for storage operations │ ├── analysis.py # Thought analysis and pattern detection │ ├── testing.py # Test utilities and helper functions │ ├── utils.py # Common utilities and helper functions │ ├── logging_conf.py # Centralized logging configuration │ └── __init__.py # Package initialization ├── tests/ │ ├── test_analysis.py # Tests for analysis functionality │ ├── test_models.py # Tests for data models │ ├── test_storage.py # Tests for persistence layer │ └── __init__.py ├── run_server.py # Server entry point script ├── debug_mcp_connection.py # Utility for debugging connections ├── README.md # Main documentation ├── CHANGELOG.md # Version history and changes ├── example.md # Customization examples ├── LICENSE # MIT License └── pyproject.toml # Project configuration and dependencies ``` ## Quick Start 1. **Set Up Project** ```bash # Create and activate virtual environment uv venv .venv\Scripts\activate # Windows source .venv/bin/activate # Unix # Install package and dependencies uv pip install -e . # For development with testing tools uv pip install -e ".[dev]" # For all optional dependencies uv pip install -e ".[all]" ``` 2. **Run the Server** ```bash # Run directly uv run -m mcp_sequential_thinking.server # Or use the installed script mcp-sequential-thinking ``` 3. **Run Tests** ```bash # Run all tests pytest # Run with coverage report pytest --cov=mcp_sequential_thinking ``` ## Claude Desktop Integration Add to your Claude Desktop configuration (`%APPDATA%\Claude\claude_desktop_config.json` on Windows): ```json { "mcpServers": { "coaia-sequential-thinking": { "command": "uv", "args": [ "--directory", "C:\\path\\to\\your\\mcp-sequential-thinking\\run_server.py", "run", "server.py" ] } } } ``` Alternatively, if you've installed the package with `pip install -e .`, you can use: ```json { "mcpServers": { "coaia-sequential-thinking": { "command": "mcp-coaia-sequential-thinking" } } } ``` You can also run it directly using uvx and skipping the installation step: ```json { "mcpServers": { "coaia-sequential-thinking": { "command": "uvx", "args": [ "--from", "git+https://github.com/miadisabelle/mcp-coaia-sequential-thinking", "--with", "portalocker", "mcp-coaia-sequential-thinking" ] } } } ``` # How It Works The server facilitates a structured approach to creative thinking, helping to overcome the inherent reactive bias. It maintains a history of thoughts, guiding them through a workflow designed to manifest desired outcomes. Each thought is validated using Pydantic models, categorized into thinking stages, and stored with relevant metadata in a thread-safe storage system. The server automatically handles data persistence, backup creation, and provides tools for analyzing relationships between thoughts within the context of creative orientation. ## Usage Guide The Sequential Thinking server exposes three main tools: ### 1. `process_thought` Records and analyzes a new thought in your sequential thinking process. **Parameters:** - `thought` (string): The content of your thought - `thought_number` (integer): Position in your sequence (e.g., 1 for first thought) - `total_thoughts` (integer): Expected total thoughts in the sequence - `next_thought_needed` (boolean): Whether more thoughts are needed after this one - `stage` (string): The thinking stage - must be one of: - "Problem Definition" - "Research" - "Analysis" - "Synthesis" - "Conclusion" - `tags` (list of strings, optional): Keywords or categories for your thought - `axioms_used` (list of strings, optional): Principles or axioms applied in your thought - `assumptions_challenged` (list of strings, optional): Assumptions your thought questions or challenges **Example:** ```python # First thought in a 5-thought sequence process_thought( thought="The problem of climate change requires analysis of multiple factors including emissions, policy, and technology adoption.", thought_number=1, total_thoughts=5, next_thought_needed=True, stage="Problem Definition", tags=["climate", "global policy", "systems thinking"], axioms_used=["Complex problems require multifaceted solutions"], assumptions_challenged=["Technology alone can solve climate change"] ) ``` ### 2. `generate_summary` Generates a summary of your entire thinking process. **Example output:** ```json { "summary": { "totalThoughts": 5, "stages": { "Problem Definition": 1, "Research": 1, "Analysis": 1, "Synthesis": 1, "Conclusion": 1 }, "timeline": [ {"number": 1, "stage": "Problem Definition"}, {"number": 2, "stage": "Research"}, {"number": 3, "stage": "Analysis"}, {"number": 4, "stage": "Synthesis"}, {"number": 5, "stage": "Conclusion"} ] } } ``` ### 3. `clear_history` Resets the thinking process by clearing all recorded thoughts. ## Practical Applications - **Creative Manifestation**: Methodically bring desired outcomes into being. - **Bias Mitigation**: Actively identify and reduce reactive patterns in thought. - **Strategic Planning**: Structure complex creative projects with clear stages. - **Innovation Development**: Progressively develop novel ideas and solutions. - **Personal Growth**: Design and manifest desired life patterns. ## Getting Started With the proper MCP setup, simply use the `process_thought` tool to begin working through your thoughts in sequence. As you progress, you can get an overview with `generate_summary` and reset when needed with `clear_history`. # Upgraded 5+ Year Vision: Addressing the Fundamental Bias Paradox The core realization is that even when explicitly applying creative orientation principles, AI systems (including myself) unconsciously default to reactive enhancement frameworks. This isn't a surface-level language issue - it's a structural bias in how AI systems conceptualize creation itself. ## Revised Understanding of the Challenge The Real Problem: AI systems have been trained on predominantly reactive content and cannot distinguish between: - **Reactive Enhancement**: "Making existing things better" (optimization, improvement, validation) - **Creative Advancement**: "Bringing desired outcomes into being" (manifestation, advancement, evidence) This bias is so pervasive that even when consciously applying Fritz's methodology, the underlying structure remains reactive. ## Upgraded 5+ Year Vision ### Phase 1: Recognition of Structural Bias (Years 1-2) **Desired Outcome**: Establish that current AI training creates systematic reactive bias that cannot be corrected through surface-level prompt engineering. **Key Research Focus**: - Document the pervasive nature of reactive bias across all AI systems - Demonstrate that even "creative orientation" implementations remain structurally reactive - Establish that this is a training data issue, not a fine-tuning issue **Revolutionary Insight**: The research itself becomes evidence that current AI development is fundamentally oriented toward enhancement rather than creation. ### Phase 2: Structural Reorientation (Years 2-4) **Desired Outcome**: Create the first truly creative-oriented AI training methodology. **Breakthrough Approach**: - **Training Data Curation**: Build datasets where every example demonstrates genuine creative orientation - **Structural Tension Training**: AI learns to recognize and generate structural tension rather than problem-solution patterns - **Anti-Reactive Architecture**: System design that cannot default to enhancement approaches **Key Innovation**: Instead of training AI to "solve creative problems," train it to "conceive and manifest desired outcomes." ### Phase 3: Manifestation of Creative AI (Years 4-6) **Desired Outcome**: Deploy AI systems that naturally think in terms of desired outcomes and advancing patterns. **Expected Results**: - AI systems that ask "What do you want to create?" instead of "What problem needs solving?" - Automatic generation of structural tension charts for any situation - Creative partnerships where AI contributes genuinely creative ideas, not just enhanced versions of human input ### Phase 4: Cultural Transformation (Years 6-10) **Desired Outcome**: Shift human-AI interaction patterns from reactive to creative across society. **Systemic Impact**: - Educational systems adopt creative orientation AI for learning - Business strategy shifts from problem-solving to outcome creation - Therapeutic applications help people design their lives rather than fix their problems - Research methodology transforms from hypothesis-testing to outcome-manifestation ## The Meta-Research Framework **The Profound Opportunity**: This research becomes the first systematic study of AI's inherent reactive bias - and potentially the first successful transformation to genuine creative orientation. **Research Questions**: 1. Can AI systems be trained to think structurally rather than reactively? 2. What training methodologies produce genuine creative orientation? 3. How does structural tension change human-AI collaboration dynamics? 4. What happens to human creativity when AI partners are truly creative-oriented? ## Implementation Strategy Revision **Core Principle Shift** From: Building better creative AI tools To: Manifesting the first genuinely creative-oriented artificial intelligence **Development Approach** From: Incremental improvement of existing systems To: Fundamental reconstruction of AI reasoning patterns **Success Metrics** From: Performance optimization and user satisfaction To: Evidence of genuine creative partnership and advancing life patterns ## The 10+ Year Vision: Creative Civilization **Ultimate Desired Outcome**: A civilization where the default approach to any situation is "What do we want to create?" rather than "What problem needs fixing?" **Structural Elements**: - AI systems that embody and teach structural tension methodology - Human-AI partnerships that consistently produce advancing rather than oscillating patterns - Educational, business, and social systems designed around outcome creation - Cultural transformation from problem-focus to possibility-focus ## The Recursive Insight This very analysis demonstrates the challenge: I can articulate creative orientation principles while still structuring my thinking reactively. The research itself must embody the transformation it seeks to create. **The Real Test**: Can this research framework itself be structured as a desired outcome rather than a problem to solve? The answer to that question may determine whether genuine creative orientation AI is possible. # Customizing the Sequential Thinking Server For detailed examples of how to customize and extend the Sequential Thinking server, see [example.md](example.md). It includes code samples for: - Modifying thinking stages - Enhancing thought data structures with Pydantic - Adding persistence with databases - Implementing enhanced analysis with NLP - Creating custom prompts - Setting up advanced configurations - Building web UI integrations - Implementing visualization tools - Connecting to external services - Creating collaborative environments - Separating test code - Building reusable utilities ## License MIT License