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memory_report_outcome

Report whether a recalled fact was useful, misleading, irrelevant, or wrong to improve future memory ranking and review.

Instructions

Report the downstream outcome of using a recalled fact — useful, misleading, irrelevant, or wrong. Writes a telemetry outcome_event row that future ranking and review work can aggregate. Unlike memory_mark_useful (positive-only, bumps a counter), this records a richer signal including negative outcomes and optional notes. Use this when you can confidently judge whether a fact actually helped: 'useful' = helped you complete the task; 'misleading' = pointed in a wrong direction; 'irrelevant' = matched semantically but didn't help; 'wrong' = factually incorrect (also consider memory_forget for clearly wrong facts).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
notesNoOptional short context for the outcome (e.g. 'API moved in v2', 'wrong port number'). Stored on the telemetry event for future analysis.
fact_idYesID of the fact whose outcome you are reporting.
outcomeYesHow the fact actually played out. 'useful' = helped you finish the task; 'misleading' = sent you the wrong way; 'irrelevant' = semantically matched but didn't help; 'wrong' = factually incorrect.
workspace_idNo[Removed in v0.4.0] No-op.
Behavior4/5

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

Discloses that it writes a telemetry outcome_event row for aggregation. With no annotations provided, this covers the core behavioral trait (write side effect) but omits details like authentication or rate limits.

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?

Four sentences that are dense with information: purpose, telemetry side effect, differentiation from sibling, and usage guidelines. No wasted words.

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

Completeness4/5

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

Given 4 params, no output schema, and no annotations, the description adequately covers what the tool does and when to use it. Could mention return value or error conditions, but not critical for a telemetry reporting tool.

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

Parameters4/5

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

Schema coverage is 100%. Description adds value by explaining the meaning of each outcome enum and the purpose of notes. It also clarifies workspace_id is a no-op.

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?

Clear verb 'report' and resource 'outcome of a recalled fact'. Distinguishes from sibling 'memory_mark_useful' by noting it handles negative signals and richer data.

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?

Explicitly states when to use this tool vs 'memory_mark_useful', and defines when each outcome value is appropriate (useful, misleading, irrelevant, wrong).

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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