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cuba_eco

Provide reinforcement feedback to adjust memory observation importance and manage quarantine gates to control which memories are retrievable.

Instructions

RLHF feedback: positive boosts importance (Oja's rule), negative decreases, correct updates content. Also the quarantine gate: 'pending' lists memories withheld from search because they came from untrusted text, 'promote' makes one retrievable, 'quarantine' withdraws one.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax rows for the 'pending' listing (default 20, max 200)
actionYesFeedback type, or a quarantine transition: promote/quarantine flip one observation's retrievability; pending lists what is currently withheld.
correctionNoNew content (for correct action)
entity_nameNoTarget entity
observation_idNoTarget observation UUID
Behavior3/5

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

With no annotations, the description carries full burden. It discloses that positive/negative affect importance (Oja's rule), correct updates content, and quarantine actions change retrievability. However, it omits side effects like whether changes are reversible, rate limits, or interaction with other tools. Not contradictory but incomplete.

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?

Two sentences pack multiple concepts, but they are front-loaded with the main purpose. No fluff. Could be clearer by separating feedback and quarantine into bullet points, but no waste.

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?

Despite 100% schema coverage and 5 parameters, the description fails to explain how parameters like entity_name and observation_id relate to each action, or what the tool returns (no output schema). The agent must guess parameter usage for each action, making the description insufficient for correct invocation.

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 coverage is 100%, so parameters are already documented. The description adds context about Oja's rule and quarantine semantics, but does not clarify action-parameter dependencies (e.g., which parameters are needed for each action). Value added is marginal.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool handles RLHF feedback (positive/negative/correct) and quarantine gate actions (pending/promote/quarantine), specifying verbs like 'boosts importance', 'decreases', 'updates content', 'lists', 'promote', 'quarantine', and resources (memories/observations). It distinguishes the two main sub-functions, though the jargon 'Oja's rule' may reduce clarity for some agents.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus its 28 siblings (e.g., cuba_calibrar, cuba_cronica). No prerequisites, success criteria, or examples of when each action is appropriate are provided. The agent must infer from action names alone.

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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