@emmahyde/thinking-patterns/dist/src/schemas/MetacognitiveMonitoringSchema.js

MCP server combining systematic thinking, mental models, debugging approaches, and stochastic algorithms for comprehensive cognitive pattern support

import { z } from 'zod';
import { ToolUsageHistorySchema } from './ToolSchemas.js';
/**
 * Metacognitive Monitoring Schema
 *
 * Defines the structure for self-assessment of knowledge and reasoning quality.
 * Supports metacognitive awareness, knowledge assessment, claim validation,
 * and reasoning quality monitoring for improved decision-making.
 */
// Metacognitive Monitoring Schema
export const KnowledgeAssessmentSchema = z.object({
    domain: z.string().describe("The specific domain or topic being assessed."),
    knowledgeLevel: z.enum(["expert", "proficient", "familiar", "basic", "minimal", "none"]).describe("The self-assessed level of knowledge in this domain."),
    confidenceScore: z.number().min(0).max(1).describe("A confidence score (0-1) in the assessed knowledge level."),
    supportingEvidence: z.string().describe("Evidence to support the assessed knowledge level."),
    knownLimitations: z.array(z.string()).describe("A list of known limitations or gaps in knowledge."),
    relevantTrainingCutoff: z.string().optional().describe("The date of the last relevant training or data cutoff."),
});
export const ClaimAssessmentSchema = z.object({
    claim: z.string().describe("The specific claim being assessed."),
    status: z.enum(["fact", "inference", "speculation", "uncertain"]).describe("The assessed status of the claim."),
    confidenceScore: z.number().min(0).max(1).describe("A confidence score (0-1) in the assessed status of the claim."),
    evidenceBasis: z.string().describe("The evidence supporting the assessment of the claim."),
    falsifiabilityCriteria: z.string().optional().describe("Criteria that could be used to falsify the claim."),
    alternativeInterpretations: z.array(z.string()).optional().describe("A list of alternative interpretations of the evidence."),
});
export const ReasoningAssessmentSchema = z.object({
    step: z.string().describe("The specific step in the reasoning process being assessed."),
    potentialBiases: z.array(z.string()).describe("A list of potential cognitive biases that may be influencing the reasoning."),
    assumptions: z.array(z.string()).describe("A list of assumptions being made in this reasoning step."),
    logicalValidity: z.number().min(0).max(1).describe("A score (0-1) for the logical validity of the reasoning step."),
    inferenceStrength: z.number().min(0).max(1).describe("A score (0-1) for the strength of the inference."),
});
export const MetacognitiveMonitoringSchema = z.object({
    task: z.string().describe("The overall task or problem being addressed."),
    stage: z.enum(["knowledge-assessment", "planning", "execution", "monitoring", "evaluation", "reflection"]).describe("The current stage of the metacognitive process."),
    knowledgeAssessment: KnowledgeAssessmentSchema.optional().describe("An assessment of the knowledge required for the task."),
    claims: z.array(ClaimAssessmentSchema).optional().describe("A list of claims being made and assessed."),
    reasoningSteps: z.array(ReasoningAssessmentSchema).optional().describe("A list of reasoning steps being assessed."),
    overallConfidence: z.number().min(0).max(1).describe("An overall confidence score (0-1) in the ability to complete the task successfully."),
    uncertaintyAreas: z.array(z.string()).describe("A list of areas where there is significant uncertainty."),
    recommendedApproach: z.string().describe("The recommended approach to address the task, given the metacognitive assessment."),
    monitoringId: z.string().describe("A unique identifier for this monitoring session."),
    iteration: z.number().describe("The iteration number of this monitoring session."),
    suggestedAssessments: z.array(z.enum(["knowledge", "claim", "reasoning", "overall"])).optional().describe("A list of suggested next assessments."),
    previousSteps: z.array(z.string()).optional().describe("A record of the steps that have already been completed."),
    remainingSteps: z.array(z.string()).optional().describe("A high-level list of the steps that are yet to be taken."),
    toolUsageHistory: z.array(ToolUsageHistorySchema).optional().describe("A log of the tools that have been used so far, along with their effectiveness."),
    nextAssessmentNeeded: z.boolean().describe("A flag indicating whether another assessment is required."),
});