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lore_update

Send ratings for memories and links to adjust their scores and improve retrieval accuracy.

Instructions

Rate memories and links after using them. Drives the quality signal loop.

Each memory_feedback dict: {id (str), useful (bool), confidence (int 1-10)}. Each link_feedback dict: {id (str), useful (bool), confidence (int 1-10)}.

useful=True bumps score; useful=False deducts. Confidence scales the delta. Repeated useful=False with low confidence triggers soft-delete. Call after every lore_search to keep scores calibrated.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
memory_feedbackNo
link_feedbackNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description fully explains behavior: the effect of useful=True/False, confidence scaling, and soft-delete on repeated false low confidence. It omits error handling and permissions but covers core behavioral traits adequately.

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?

Five sentences, each necessary: purpose first, then detailed feedback structure, then behavioral effects and usage advice. No fluff.

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 that an output schema exists, the description need not explain return values. It covers parameter structure, behavioral effects, and usage context completely for a feedback loop tool.

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?

Schema coverage is 0%, but the description provides complete structure for both parameters, specifying keys (id, useful, confidence) and types/range. This fully compensates for the schema's lack of detail.

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 explicitly states the verb 'rate' and the resources 'memories' and 'links', clearly indicating the tool's function. It distinguishes itself from siblings by specifying it drives the 'quality signal loop' and is used after lore_search.

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 states to call after every lore_search to keep scores calibrated, providing clear when-to-use guidance. It does not include when-not-to-use or alternative tools, but the context is strong enough for correct invocation.

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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