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archive_old_memories

Archive AI memories by age and importance to manage long-term data storage, enabling efficient memory organization and continuity.

Instructions

Archive old memories based on age and importance criteria

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
days_oldNoMinimum age in days for archival
importance_thresholdNoMaximum importance for archival

Implementation Reference

  • The main implementation of archiveOldMemories method that archives old memories based on age, importance, and access count criteria. Updates memory status to 'archived' and records archival events in the memoryChanges table.
    async archiveOldMemories(daysOld = 365, importanceThreshold = 0.3) {
      try {
        const cutoffDate = new Date(Date.now() - daysOld * 24 * 60 * 60 * 1000);
        
        const archivedMemories = await this.db
          .update(schema.memories)
          .set({ status: 'archived' })
          .where(
            and(
              eq(schema.memories.status, 'active'),
              lt(schema.memories.createdAt, cutoffDate),
              lt(schema.memories.importance, importanceThreshold),
              lt(schema.memories.accessCount, 5)
            )
          )
          .returning({
            id: schema.memories.id,
            content: schema.memories.content,
            type: schema.memories.type
          });
    
        // Record archival events
        for (const memory of archivedMemories) {
          await this.db.insert(schema.memoryChanges).values({
            memoryId: memory.id,
            changeType: 'archival',
            newValue: { reason: 'Archived due to age and low importance' }
          });
        }
    
        return archivedMemories;
      } catch (error) {
        console.warn('Memory archival failed:', error.message);
        return [];
      }
    }
  • mcp.js:634-639 (registration)
    MCP tool handler registration that maps the 'archive_old_memories' tool name to the MemoryManager.archiveOldMemories method, extracting days_old and importance_threshold parameters from args.
    case "archive_old_memories":
      const archivedMemories = await memoryManager.archiveOldMemories(
        args.days_old || 365,
        args.importance_threshold || 0.3
      );
      return { content: [{ type: "text", text: JSON.stringify(archivedMemories, null, 2) }] };
  • Input schema definition for the archive_old_memories tool, specifying days_old (integer, default 365) and importance_threshold (number, default 0.3) parameters with descriptions.
    {
      name: "archive_old_memories",
      description: "Archive old memories based on age and importance criteria",
      inputSchema: {
        type: "object",
        properties: {
          days_old: {
            type: "integer",
            description: "Minimum age in days for archival",
            default: 365
          },
          importance_threshold: {
            type: "number",
  • Duplicate schema definition for archive_old_memories tool with the same parameter structure as in mcp.js, defining the input validation schema for the tool.
    {
      name: "archive_old_memories",
      description: "Archive old memories based on age and importance criteria",
      inputSchema: {
        type: "object",
        properties: {
          days_old: {
            type: "integer",
            description: "Minimum age in days for archival",
            default: 365
          },
          importance_threshold: {
            type: "number",
            description: "Maximum importance for archival",
            default: 0.3
          }
        }
      }
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It implies a mutation operation ('Archive') but doesn't specify whether this is destructive, reversible, requires permissions, or has side effects like rate limits. The description adds minimal context beyond the basic action, leaving key behavioral traits undefined.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core action without unnecessary words. It directly states the purpose and criteria, making it easy to parse quickly, with no redundant information or structural issues.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what 'Archive' entails (e.g., storage location, reversibility), potential impacts, or return values. Given the complexity and lack of structured data, more detail is needed to adequately guide usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, clearly documenting both parameters with defaults. The description adds value by mentioning 'age and importance criteria', which aligns with the parameters but doesn't provide additional semantics like how importance is measured or archival outcomes. Baseline 3 is appropriate as the schema handles most documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Archive') and resource ('old memories'), specifying the action and target. It distinguishes from siblings like 'prune_memories' by focusing on archival based on criteria rather than deletion or other operations. However, it doesn't explicitly differentiate from all siblings, such as 'cleanup_expired_working_memory', which might have overlapping functions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'prune_memories' or 'cleanup_expired_working_memory'. It lacks context about prerequisites, such as whether memories must be inactive or if archival is reversible, and offers no explicit alternatives or exclusions for usage scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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