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memory_mark_useful

Report that a recalled fact helped answer a question or complete a task, increasing its usefulness score for future retrieval.

Instructions

Report that a previously recalled fact actually helped you answer the user's question or complete a task. Bumps a 'useful_count' counter on the fact and writes a telemetry feedback_event row that future ranking work can aggregate. Call this AFTER you used a fact from memory_recall / memory_entity and confirmed it was helpful — not for every recalled fact. If the fact was wrong or misleading, use memory_report_outcome with outcome='wrong' or 'misleading' instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNoOptional short note about how the fact was useful (e.g. 'unblocked auth debug'). Stored on the telemetry event for future analysis.
fact_idYesID of the fact that was useful (from memory_recall or memory_entity).
workspace_idNo[Removed in v0.4.0] No-op.
Behavior5/5

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

No annotations provided, but description fully discloses side effects: bumps 'useful_count' counter, writes telemetry feedback event, and notes workspace_id is a no-op. This meets the burden for transparency.

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?

Single concise paragraph, front-loaded with purpose, no wasted words. Every sentence adds essential context.

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?

Given no output schema, description sufficiently covers purpose, timing, alternatives, and side effects. An agent can correctly decide when and how to invoke this 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%, baseline 3. Description adds value by clarifying fact_id source (memory_recall/memory_entity), note purpose (telemetry), and workspace_id deprecation. Extra context justifies a 4.

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 reports a helpful recalled fact, with specific verb 'Report' and resource 'fact'. It explicitly distinguishes from sibling 'memory_report_outcome' by stating alternative for wrong/misleading facts.

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?

Usage guidelines are explicit: call only after confirming fact was helpful, not for every recalled fact. It provides a clear alternative: use memory_report_outcome for wrong/misleading facts.

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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