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Log RLHF Feedback

neuroverse_feedback

Rate model responses by intent to tune AI agents with reinforcement learning from human feedback.

Instructions

Submit Reinforcement Learning from Human Feedback (RLHF) data for agent tuning.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYesThe model used
intentYesThe intent executed
ratingYesRating from 1 to 5
feedback_textNoOptional human-readable feedback
Behavior2/5

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

No behavioral traits beyond what annotations imply are disclosed. The description does not mention side effects, authorization needs, rate limits, or how data is stored.

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 sentence, front-loaded with purpose, no wasted words. Highly concise.

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

Completeness3/5

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

Minimally adequate for a simple submission tool. Lacks usage guidance and behavioral details, but parameters and annotations are sufficient for basic understanding.

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 baseline is 3. The description adds context about RLHF and agent tuning but does not significantly enhance understanding of individual parameters beyond the schema.

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 action ('submit'), the resource ('RLHF data'), and the purpose ('agent tuning'). It distinguishes itself from siblings like neuroverse_execute or neuroverse_model by focusing on feedback logging.

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 alternatives, nor any prerequisites or context for using other tools. The description only states what the tool does, not when it should be invoked.

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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