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CongBao

failure-memory

by CongBao

recall_failure_lessons

Recall up to three failure lessons from global personal memory using exact, lexical, semantic, or hybrid retrieval, and append a privacy-preserving trace.

Instructions

Recall up to three lessons from global personal memory using exact, lexical, semantic, or hybrid retrieval and append a privacy-preserving trace.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNo
textNo
top_kNo
componentNo
cause_layerNo
failure_modeNo
prevention_actionNo
controllable_causeNo
expected_invariantNo
repair_target_layerNo
Behavior4/5

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

Given the annotations provide no hints (all false), the description carries the burden of disclosing side effects. It explicitly mentions that the tool 'append[s] a privacy-preserving trace', which is a behavioral trait beyond the schema. There is no contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, dense sentence that front-loads the core purpose. It avoids unnecessary words, though 'privacy-preserving' could be considered extra detail. Overall, it is effectively concise.

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

Completeness2/5

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

This is a complex tool with 10 parameters, no output schema, and no useful annotations. The description covers only a fraction of the input space, failing to explain the structured search criteria and the required combinations (anyOf). It is not complete enough for an agent to invoke it reliably.

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

Parameters2/5

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

The schema description coverage is 0%, so the description must compensate for the 10 parameters. It hints at 'mode' through retrieval types and 'top_k' via 'up to three', but it does not explain the other eight parameters, including the structured fields like 'expected_invariant' or 'cause_layer'. This leaves significant gaps.

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 identifies the tool's action ('Recall'), the resource ('lessons from global personal memory'), and specifies the scope ('up to three'). It also lists the retrieval modes, which distinguishes it from the sibling tool 'remember_failure'.

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

Usage Guidelines3/5

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

The description implies this tool is used for recalling previously stored lessons, and the sibling name 'remember_failure' suggests the alternative is for storing. However, no explicit 'when to use' or 'when not to use' guidance is provided, so the context is only implied.

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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