AGI MCP Server

Instructions

Implementation Reference

• mcp.js:29-60 (registration)

  Tool registration and input schema definition for create_memory. Defines the tool name, description, and JSON schema with properties for type, content, embedding, importance, and metadata.

  name: "create_memory",
  description: "Create a new memory with optional type-specific metadata",
  inputSchema: {
    type: "object",
    properties: {
      type: {
        type: "string",
        enum: ["episodic", "semantic", "procedural", "strategic"],
        description: "Type of memory to create"
      },
      content: {
        type: "string",
        description: "The main content/text of the memory"
      },
      embedding: {
        type: "array",
        items: { type: "number" },
        description: "Vector embedding for the memory content"
      },
      importance: {
        type: "number",
        description: "Importance score (0.0 to 1.0)",
        default: 0.0
      },
      metadata: {
        type: "object",
        description: "Type-specific metadata (action_taken, context, confidence, etc.)",
        default: {}
      }
    },
    required: ["type", "content", "embedding"]
  }

• mcp.js:536-544 (handler)

  MCP request handler for create_memory. Extracts arguments and calls memoryManager.createMemory() with type, content, embedding, importance, and metadata, then returns the created memory as JSON.

  case "create_memory":
    const memory = await memoryManager.createMemory(
      args.type,
      args.content,
      args.embedding,
      args.importance || 0.0,
      args.metadata || {}
    );
    return { content: [{ type: "text", text: JSON.stringify(memory, null, 2) }] };

• src/memory-manager.js:20-90 (handler)

  Core implementation of createMemory method in MemoryManager class. Handles transaction to insert main memory record and type-specific details (episodic, semantic, procedural, strategic) with their respective metadata fields.

  async createMemory(type, content, embedding, importance = 0.0, metadata = {}) {
    try {
      // Start transaction
      const result = await this.db.transaction(async (tx) => {
        // Insert main memory record
        const [memory] = await tx.insert(schema.memories).values({
          type,
          content,
          embedding: embedding,
          importance,
          decayRate: metadata.decayRate || 0.01
        }).returning();
  
        // Insert type-specific details
        switch (type) {
          case 'episodic':
            await tx.insert(schema.episodicMemories).values({
              memoryId: memory.id,
              actionTaken: metadata.action_taken || null,
              context: metadata.context || null,
              result: metadata.result || null,
              emotionalValence: metadata.emotional_valence || 0.0,
              eventTime: metadata.event_time || new Date(),
              verificationStatus: metadata.verification_status || null
            });
            break;
  
          case 'semantic':
            await tx.insert(schema.semanticMemories).values({
              memoryId: memory.id,
              confidence: metadata.confidence || 0.8,
              category: metadata.category || [],
              relatedConcepts: metadata.related_concepts || [],
              sourceReferences: metadata.source_references || null,
              contradictions: metadata.contradictions || null
            });
            break;
  
          case 'procedural':
            await tx.insert(schema.proceduralMemories).values({
              memoryId: memory.id,
              steps: metadata.steps || {},
              prerequisites: metadata.prerequisites || {},
              successCount: metadata.success_count || 0,
              totalAttempts: metadata.total_attempts || 0,
              failurePoints: metadata.failure_points || null
            });
            break;
  
          case 'strategic':
            await tx.insert(schema.strategicMemories).values({
              memoryId: memory.id,
              patternDescription: metadata.pattern_description || content,
              confidenceScore: metadata.confidence_score || 0.7,
              supportingEvidence: metadata.supporting_evidence || null,
              successMetrics: metadata.success_metrics || null,
              adaptationHistory: metadata.adaptation_history || null,
              contextApplicability: metadata.context_applicability || null
            });
            break;
        }
  
        return memory;
      });
  
      return result;
    } catch (error) {
      console.error('Error creating memory:', error);
      throw error;
    }
  }

• src/db/schema.js:9-27 (schema)

  Main memories table schema definition. Defines the core memory structure with id, type, status, content, embedding, importance, access tracking, and relevance score calculation.

  export const memories = pgTable("memories", {
  	id: uuid().defaultRandom().primaryKey().notNull(),
  	createdAt: timestamp("created_at", { withTimezone: true, mode: 'string' }).default(sql`CURRENT_TIMESTAMP`),
  	updatedAt: timestamp("updated_at", { withTimezone: true, mode: 'string' }).default(sql`CURRENT_TIMESTAMP`),
  	type: memoryType().notNull(),
  	status: memoryStatus().default('active'),
  	content: text().notNull(),
  	embedding: vector({ dimensions: 1536 }).notNull(),
  	importance: doublePrecision().default(0),
  	accessCount: integer("access_count").default(0),
  	lastAccessed: timestamp("last_accessed", { withTimezone: true, mode: 'string' }),
  	decayRate: doublePrecision("decay_rate").default(0.01),
  	relevanceScore: doublePrecision("relevance_score").generatedAlwaysAs(sql`(importance * exp(((- decay_rate) * age_in_days(created_at))))`),
  }, (table) => [
  	index("memories_content_idx").using("gin", table.content.asc().nullsLast().op("gin_trgm_ops")),
  	index("memories_embedding_idx").using("ivfflat", table.embedding.asc().nullsLast().op("vector_cosine_ops")),
  	index("memories_relevance_score_idx").using("btree", table.relevanceScore.desc().nullsFirst().op("float8_ops")).where(sql`(status = 'active'::memory_status)`),
  	index("memories_status_idx").using("btree", table.status.asc().nullsLast().op("enum_ops")),
  ]);

• src/db/schema.js:58-140 (schema)

  Type-specific memory table schemas (episodic, semantic, procedural, strategic). Each extends the main memory with specialized metadata fields like emotional valence, confidence, success metrics, etc.

  export const episodicMemories = pgTable("episodic_memories", {
  	memoryId: uuid("memory_id").primaryKey().notNull(),
  	actionTaken: jsonb("action_taken"),
  	context: jsonb(),
  	result: jsonb(),
  	emotionalValence: doublePrecision("emotional_valence"),
  	verificationStatus: boolean("verification_status"),
  	eventTime: timestamp("event_time", { withTimezone: true, mode: 'string' }),
  }, (table) => [
  	foreignKey({
  			columns: [table.memoryId],
  			foreignColumns: [memories.id],
  			name: "episodic_memories_memory_id_fkey"
  		}),
  	check("valid_emotion", sql`(emotional_valence >= ('-1'::integer)::double precision) AND (emotional_valence <= (1)::double precision)`),
  ]);
  
  export const clusterActivationHistory = pgTable("cluster_activation_history", {
  	id: uuid().defaultRandom().primaryKey().notNull(),
  	clusterId: uuid("cluster_id"),
  	activatedAt: timestamp("activated_at", { withTimezone: true, mode: 'string' }).default(sql`CURRENT_TIMESTAMP`),
  	activationContext: text("activation_context"),
  	activationStrength: doublePrecision("activation_strength"),
  	coActivatedClusters: uuid("co_activated_clusters").array(),
  	resultingInsights: jsonb("resulting_insights"),
  }, (table) => [
  	foreignKey({
  			columns: [table.clusterId],
  			foreignColumns: [memoryClusters.id],
  			name: "cluster_activation_history_cluster_id_fkey"
  		}),
  ]);
  
  export const semanticMemories = pgTable("semantic_memories", {
  	memoryId: uuid("memory_id").primaryKey().notNull(),
  	confidence: doublePrecision().notNull(),
  	lastValidated: timestamp("last_validated", { withTimezone: true, mode: 'string' }),
  	sourceReferences: jsonb("source_references"),
  	contradictions: jsonb(),
  	category: text().array(),
  	relatedConcepts: text("related_concepts").array(),
  }, (table) => [
  	foreignKey({
  			columns: [table.memoryId],
  			foreignColumns: [memories.id],
  			name: "semantic_memories_memory_id_fkey"
  		}),
  	check("valid_confidence", sql`(confidence >= (0)::double precision) AND (confidence <= (1)::double precision)`),
  ]);
  
  export const proceduralMemories = pgTable("procedural_memories", {
  	memoryId: uuid("memory_id").primaryKey().notNull(),
  	steps: jsonb().notNull(),
  	prerequisites: jsonb(),
  	successCount: integer("success_count").default(0),
  	totalAttempts: integer("total_attempts").default(0),
  	successRate: doublePrecision("success_rate").generatedAlwaysAs(sql`
  CASE
      WHEN (total_attempts > 0) THEN ((success_count)::double precision / (total_attempts)::double precision)
      ELSE (0)::double precision
  END`),
  	averageDuration: interval("average_duration"),
  	failurePoints: jsonb("failure_points"),
  }, (table) => [
  	foreignKey({
  			columns: [table.memoryId],
  			foreignColumns: [memories.id],
  			name: "procedural_memories_memory_id_fkey"
  		}),
  ]);
  
  export const strategicMemories = pgTable("strategic_memories", {
  	memoryId: uuid("memory_id").primaryKey().notNull(),
  	patternDescription: text("pattern_description").notNull(),
  	supportingEvidence: jsonb("supporting_evidence"),
  	confidenceScore: doublePrecision("confidence_score"),
  	successMetrics: jsonb("success_metrics"),
  	adaptationHistory: jsonb("adaptation_history"),
  	contextApplicability: jsonb("context_applicability"),
  }, (table) => [
  	foreignKey({
  			columns: [table.memoryId],
  			foreignColumns: [memories.id],