Tianji Thinking Models

Tool Functions in Detail

Exploration Tools

• list-models: List all thinking models or filter by category
• search-models: Search thinking models by keywords
• get-categories: Get all thinking model categories
• get-model-info: Get detailed information about a thinking model
• get-related-models: Get other models related to a specific model

Problem-Solving Tools

• recommend-models-for-problem: Recommend suitable thinking models based on problem keywords
• interactive-reasoning: Interactive reasoning process guidance
• generate-validate-hypotheses: Generate multiple hypotheses for a problem and provide validation methods
• explain-reasoning-process: Explain the reasoning process of a model and the thinking patterns applied

Creation Tools

• create-thinking-model: Create a new thinking model
• update-thinking-model: Update any field of an existing thinking model
• emergent-model-design: Create new thinking models by combining existing ones
• delete-thinking-model: Delete unwanted thinking models

System and Learning Tools

• get-started-guide: Beginner's guide
• get-server-version: Get server version information
• count-models: Count the total number of current thinking models
• record-user-feedback: Record user feedback on thinking model experiences
• detect-knowledge-gap: Detect knowledge gaps in user queries
• get-model-usage-stats: Get usage statistics for thinking models
• analyze-learning-system: Analyze the status of the thinking model learning system

Use Cases

Solving Complex Problems

When facing complex problems, the system can recommend multiple thinking models to help analyze problems from different angles and avoid mental blind spots.

Improving Thinking Quality

Through structured thinking processes, avoid common cognitive biases and make more rational decisions.

Learning Thinking Models

The system not only provides definitions of thinking models but also includes detailed teaching content, application examples, and notes to help you master various thinking tools.

Creating Custom Models

When existing models cannot meet needs, you can create new thinking models or combine existing models to create innovative thinking frameworks.

Data Structure

The thinking models in the system contain the following core elements:

• Basic Information: ID, name, definition, purpose, category, etc.
• Application Guide: Usage steps, interaction methods, constraints, etc.
• Teaching Content: Popular science teaching, examples, etc.
• Warning Information: Limitations, common pitfalls, etc.
• Visualizations: Flowcharts, tables, bar charts, lists, etc.

Technical Implementation

• Server Core: MCP protocol-based server implementation
• Parameter Schemas: Using Zod library to define and validate tool parameters
• Data Storage: Using JSON files to store thinking model data
• Communication Mode: Communication through standard input/output, following MCP protocol specifications

Integration Methods

Can be run as a standalone service, or integrated into clients that support the MCP protocol, such as various AI assistants and development environments.