What reasoning strategies does Deep Thinker support?
Deep Thinker supports Sequential, Dialectic (thesis→antithesis→synthesis), Parallel, Analogical, and Abductive (inference to best explanation) strategies.
Deep Thinker MCP Server
@hubinoretros
基本信息
关于 Deep Thinker MCP Server
Advanced cognitive thinking MCP server with DAG-based thought graph, multiple reasoning strategies, metacognition, and self-evaluation.
Deep Thinker MCP Server 配置
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"deep-thinker": {
"command": "npx",
"args": [
"-y",
"deep-thinker"
]
}
}
}Deep Thinker MCP Server 工具
17Add a thought to the cognitive graph using the current strategy. Supports sequential, dialectic, parallel, analogical, and abductive reasoning strategies. Each thought becomes a node in a DAG with confidence scoring, edges, and metadata.
Evaluate the thinking process: score confidence, generate critiques, and assess overall graph health. Provides detailed analysis of weak spots and strong reasoning paths.
Metacognitive operations: view the current thinking state, get strategy suggestions, switch strategies, and receive guidance on improving reasoning. The system automatically detects stuck states and recommends actions.
Query and visualize the thought graph. View the DAG structure, find paths, inspect branches, and get statistics.
Prune and optimize the thought graph. Remove dead ends, consolidate redundant branches, and optimize reasoning paths. Helps maintain graph efficiency during deep reasoning.
Reset the thought graph and metacognitive state. Start a fresh reasoning session, save current session, or resume a saved session.
Generate visual representation of the thought graph as SVG or ASCII. Supports highlighting paths, branches, and confidence levels.
Generate counterarguments and opposing viewpoints for a given thought. Automatically creates antithesis nodes.
Combine insights from multiple domains to generate novel perspectives. Creates analogical mappings between domains.
Project thoughts into future or past scenarios. Analyze how conclusions change over time.
Evaluate a thought or decision through multiple ethical frameworks (deontological, consequentialist, virtue ethics, rights-based).
Analyze emotional tone, stakeholder emotions, and social dynamics of thoughts. Provides empathy and persuasion insights.
Generate human-understandable explanation of a decision path. Shows which factors contributed most and why.
Analyze social impact, stakeholder emotions, group cohesion, and persuasion effectiveness of a thought or decision.
Node Zero (PromptOptimizer): Transform vague/raw prompts into optimized Super Prompts with routing recommendations. Entry point for the reasoning DAG.
Discover deep-thinker tools and learn usage workflows. Shows tools grouped by category with quick-start examples.
Analyze the entire thought graph and produce a comprehensive summary-conclusion with action items and graph health report.
概览
What is Deep Thinker?
Deep Thinker is an advanced cognitive MCP server with DAG-based thought graphs, multiple reasoning strategies, metacognition, and self-evaluation. It extends sequential thinking by supporting branching, merging, and cross-edges.
How to use Deep Thinker?
—
Key features of Deep Thinker
- DAG-based thought graph with branching, merging, and cross-edges
- Five reasoning strategies: Sequential, Dialectic, Parallel, Analogical, Abductive
- Multi-factor confidence scoring with support/contradiction analysis
- Automatic self-critique with severity levels and confidence adjustments
- Metacognitive engine detecting stuck states and stagnant reasoning
- Knowledge integration for attaching, gap detection, and consistency validation
- Thought pruning: dead-end detection, redundancy removal, branch elimination
Use cases of Deep Thinker
- Complex problem solving that requires structured deep reasoning
- Scenarios needing dialectical or abductive inference for best explanations
- Tasks where self-evaluation and confidence calibration are critical
- Exploratory analysis with branching and merging of multiple reasoning paths
- Knowledge-intensive reasoning requiring external knowledge integration and validation
常见问题
How does the metacognitive engine work?
The metacognitive engine detects stuck states, stagnation, and declining confidence, then suggests strategy switches and corrective actions.
What is thought pruning and why is it needed?
Thought pruning removes dead-ends, redundant thoughts, and deep unproductive branches to optimize the reasoning graph.
How is confidence scoring calculated?
Confidence scoring uses multi-factor evaluation including support/contradiction analysis, depth penalties, and knowledge integration boosts.
How does Deep Thinker differ from sequential-thinking MCP?
Deep Thinker uses a DAG-based thought graph with branching, merging, and cross-edges rather than a linear chain, and adds multiple reasoning strategies, confidence scoring, self-critique, metacognition, knowledge integration, and pruning.
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