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memory_record_use

Record whether a retrieved memory was ignored, contradicted, or corrected to adjust its future relevance and flag unresolved contradictions.

Instructions

Override the auto-applied outcome. Default behavior: every memory_search hit settles as applied at turn end (excerpts when the reply used it, auto=true otherwise). The common case handles itself — only call this tool when the model needs to record one of the three actionable outcomes below.

Outcome

When

What it does

ignored

retrieved but off-topic

annotates later hits; under outcome_demotion, mild 30d demotion (applied/update/verify clears)

contradicted

stored fact disagreed AND not yet fixed

raises the unresolved-contradiction flag until a later memory_update or memory_verify clears it; demotes 2x under the same flag

corrected

drifted and you fixed it inline (memory_update and/or memory_verify already called)

audit-only; does NOT raise the flag, never demotes

applied is also accepted explicitly (rarely needed — auto handles it; call only to force-commit early).

Parameters:

  • memory_ids: list (1+).

  • outcome: see table above.

  • note (optional, ≤800 chars): free-form context.

  • claim_excerpts (optional): list parallel to memory_ids (same length, None slots OK) carrying the load-bearing phrase that shaped the response. ≤500 chars per excerpt. Pass None for 'no specific claim' — empty strings are rejected (they're ambiguous: missing claim vs. zero-length claim). Especially useful on contradicted / corrected so the audit log records WHICH claim was wrong, not just that the memory drifted. Surfaces back in recent_negative_outcomes on later search hits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNo
outcomeYes
memory_idsYes
claim_excerptsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations, the description fully discloses side effects: demotion under `outcome_demotion`, unresolved-contradiction flag, audit-only for `corrected`, and the auto-applied default. It also explains `claim_excerpts` constraints (empty strings rejected) and their surfacing in `recent_negative_outcomes`.

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?

Although long, the description is well-structured with a markdown table and bullet list. The core purpose is front-loaded, and every sentence adds value, making the length justified.

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?

The description covers the decision process, side effects, parameter constraints, and edge cases. With an output schema present, return values are unnecessary to explain. It is complete for the tool's complexity.

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?

The schema has no parameter descriptions (0% coverage), so the description fully compensates. The outcome table defines meaningful values, `claim_excerpts` gets detailed parallel-list semantics, and constraints (length limits, None vs empty) are explained.

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?

The description clearly states the tool's purpose: 'Override the auto-applied outcome.' It explains default behavior and when to call, distinguishing it from siblings that modify memory content. The table of outcomes further clarifies its specific scope.

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

Usage Guidelines5/5

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

The description explicitly says 'only call this tool when the model needs to record one of the three actionable outcomes below' and explains that the common case handles itself. It also notes that explicit `applied` is rarely needed, giving clear when-to/not-to-use guidance.

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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