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Multi-strategy reasoning and problem-solving server implementing the Model Context Protocol. Provides structured cognitive processing with 20+ reasoning strategies, cognitive state management, and unified reasoning chains.
The MetaCognition MCP Server orchestrates multiple reasoning strategies for complex problem-solving and analysis. It manages reasoning chains, cognitive states, and provides tools for autonomous decision-making, debugging, research, and optimization.
| Tool | Function |
|---|---|
metacognition | Multi-strategy reasoning and cognitive state management |
autonomous | Decision engine with comprehensive strategy orchestration |
debugger | Code analysis and error resolution |
markdown_master | Markdown content creation and refactoring |
research_pro | Research synthesis with online search integration |
deep_analysis | Codebase and system analysis |
optimus_prime | Performance and resource optimization |
wildcard | Dynamic strategy selection |
Logical: Abductive, Deductive, Inductive, Analogical, ForwardChaining, BackwardChaining, Defeasible
Probabilistic: Bayesian, FuzzyLogic
Structural: PatternAnalysis, ConstraintSatisfaction
Causal: Causal, Counterfactual, Empirical
Specialized: Financial, Ethical, Sequential, CaseBased, HypothesisGeneration
Control: MetacognitiveControl
npm install @modelcontextprotocol/metacog
npm run build
npm start
docker build -t mcp/metacog
docker run --rm -i mcp/metacog
{
"mcpServers": {
"metacognition": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/metacog"]
}
}
}
{
"mcp": {
"servers": {
"metacognition": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/metacog"]
}
}
}
}
{
"thought": "Analyzing market data for trend identification",
"thoughtNumber": 1,
"totalThoughts": 3,
"nextThoughtNeeded": true,
"strategy": "Bayesian",
"strategy_input_data": {
"prior": 0.6,
"evidence_threshold": 0.8
}
}
{
"thought": "Exploring multiple causal hypotheses",
"thoughtNumber": 1,
"totalThoughts": 4,
"nextThoughtNeeded": true,
"cognitive_superposition_concepts": [
"supply chain disruption",
"demand fluctuation",
"competitive pressure",
"regulatory changes"
]
}
{
"thought": "Comprehensive problem analysis",
"thoughtNumber": 1,
"totalThoughts": 1,
"nextThoughtNeeded": false,
"unified_reasoning_chain": true,
"strategies": ["Causal", "Bayesian", "Sequential"],
"convergence_target": 0.95,
"cognitive_enhancement": true
}
thought (string): Current reasoning step or analysisthoughtNumber (integer): Current step numbertotalThoughts (integer): Estimated total steps (adjustable)nextThoughtNeeded (boolean): Whether additional steps are requiredcognitive_superposition_concepts (array): Create parallel hypothesis statescognitive_resolve_state_id (string): Collapse superposition to single conceptstrategy (enum): Select specific reasoning strategyunified_reasoning_chain (boolean): Execute multi-strategy reasoningconvergence_target (number): Target confidence level (0.0-1.0)Different strategies accept specific input parameters via strategy_input_data:
{ prior: number, evidence_threshold: number }{ cause: string, effect: string }{ original_condition: string, altered_condition: string }{ market_data: object, risk_tolerance: number }{ time_horizon: string, world_branches: boolean }DISABLE_THOUGHT_LOGGING: Disable console output of reasoning stepsCOGNITIVE_ENHANCEMENT: Enable/disable cognitive superposition featuresCONVERGENCE_TARGET: Default convergence threshold for reasoning chainsnpm install
npm run build
npm test
npm run test:strategies # Strategy validation
npm run test:cognitive # Cognitive features
npm run benchmark # Performance tests
MIT & Apache-2.0
FAQs
MCP server for meta-cognitive reasoning strategies and problem resolution.
The npm package metacog receives a total of 2 weekly downloads. As such, metacog popularity was classified as not popular.
We found that metacog demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 0 open source maintainers collaborating on the project.
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