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Record Recall Feedback

record_recall_feedback

Records corrections and feedback when memory recall results are missing or wrong. Captures missing memories and corrections to improve future recall accuracy.

Instructions

Record recall corrections. Call this when the user says relevant context was missing or wrong, or supplies memories that should have been found.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
notesNoFreeform notes explaining recall feedback or corrections.
queryYesNatural-language question or search text.
metadata_jsonNoOptional JSON object string with extra metadata for the operation.{}
missing_ids_jsonNoJSON array of relevant memory IDs that were missing from recall results.[]
returned_ids_jsonNoJSON array of memory IDs returned by the recall query.[]
corrected_ids_jsonNoJSON array of memory IDs that should have been returned for the recall query.[]
missing_memories_jsonNoJSON array of memory snippets or objects that should exist but were missing.[]

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Annotations declare readOnlyHint=false and destructiveHint=false, so the description doesn't need to re-state safety. It adds that the tool records corrections, which is consistent but doesn't disclose additional behavioral details like potential side effects or error states.

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 usage context. No unnecessary words or repetition.

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?

With an output schema present and full parameter coverage, the description adequately covers usage context. A brief note on the purpose of key parameters like missing_ids_json could elevate completeness, but it's not critical.

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?

Schema description coverage is 100%, so the baseline is 3. The description adds no parameter-specific meaning beyond what the schema already provides.

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 records recall corrections and specifies exactly when to call it: when the user reports missing or wrong context, or supplies memories that should have been found. This distinguishes it from sibling tools.

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?

The description explicitly tells when to use the tool ('Call this when...'), providing clear context. It doesn't mention when not to use it, but the positive guidance is strong and leaves little ambiguity.

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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