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archive_learning

Archive outdated or irrelevant learnings to remove them from recall and search results while preserving an audit trail with timestamp and optional reason.

Instructions

Archives a learning (scar/win/pattern) by setting is_active=false and recording archived_at timestamp. Archived learnings are excluded from recall and search results but preserved for audit trail.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "id": "<id>"
    },
    "name": "archive_learning"
  }
}

Input Schema

Name	Required	Description	Default
`id`	Yes	UUID or short ID prefix of the learning to archive (e.g., the 8-char prefix shown by recall/search)
`reason`	No	Optional reason for archiving (e.g., 'superseded by PROJ-123', 'no longer relevant')

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.6.4Jun 25, 2026

TDQS

A4.6/5.0

Behavior4/5

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

With no annotations, description fully discloses soft-delete behavior (set active flag, timestamp, exclusion from search). Minor omission: no mention of reinstatement possibility, but sufficient for common use.

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?

Two concise sentences, front-loaded with action and effect, no wasted words.

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

Completeness5/5

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

Adequately covers purpose, mechanism, and impact on other tools. No output schema needed for this simple operation.

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

Parameters5/5

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

Schema coverage is 100%, and description adds value with examples (UUID prefix, reason example) beyond the schema's basic descriptions.

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

Purpose5/5

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

Clearly states it archives a learning by setting is_active=false and recording timestamp, and explains impact on recall/search. Distinguishes from sibling tools like create_learning or recall.

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

Usage Guidelines4/5

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

Tells when to use (to archive a learning) and consequences (excluded from recall/search, preserved for audit). Lacks explicit when-not or alternative tools, but context is clear.

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