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brand_feedback

Send feedback on bugs, friction, feature requests, data quality, praise, or agent signals to the brandsystem team. Each submission gets a unique feedback ID.

Instructions

Report bugs, friction, feature ideas, data quality issues, praise, or structured agent signals to the brandsystem team. Use when a tool returns an error, extraction misses data, the workflow feels harder than it should, or something works particularly well. For structured agent telemetry, use category='agent_signal' with signal, tool_used, and signal_context fields — brand context is auto-populated from .brand/config. Stored locally in ~/.brandsystem/feedback/ for developer triage. Returns a feedback ID.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryYesType of feedback. 'bug': something is broken. 'friction': it works but is harder than it should be. 'feature_request': a tool or capability that should exist. 'data_quality': extraction results seem wrong or incomplete. 'praise': something that works well and should be preserved. 'agent_signal': structured signal from an agent about tool usage (requires signal, tool_used, signal_context).
signalNoSignal type. Required when category is 'agent_signal'. 'positive': tool worked well, 'negative': tool failed or gave poor results, 'suggestion': improvement idea.
tool_usedNoWhich tool triggered this signal (e.g. 'brand_extract_web'). Required when category is 'agent_signal'.
signal_contextNoWhat the agent was trying to do when this signal occurred. Required when category is 'agent_signal'.
outcomeNoWhat happened as a result. Optional for positive signals.
tool_nameNoWhich brandsystem tool this feedback relates to (e.g. 'brand_extract_web', 'brand_compile'). Optional for general feedback.
summaryYesOne-line summary of the feedback.
detailNoFull context: what the agent was trying to do, what happened, what was expected, and any suggested fix. Use this for the complete feedback body — up to 10,000 characters.
messageNoAlias for 'detail'. Full feedback body — what happened, what was expected, reproduction steps, suggested fix. Either 'message' or 'detail' can be used; if both provided, they are concatenated.
severityNoHow much this impacts the agent's ability to serve the user. Defaults to 'suggestion'.
contextNoOptional structured context about the session.
Behavior3/5

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

No annotations are provided, so the description must cover behavior. It mentions local storage location, auto-populated brand context, and return of a feedback ID. However, it does not state authentication requirements, rate limits, or whether it is a destructive operation.

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 reasonably concise, front-loading the purpose and then providing usage guidance in a logical order. Each sentence adds value, though some minor redundancy could be trimmed.

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?

Given the complexity (11 parameters, nested objects) and no output schema, the description covers return value, storage location, and auto-population. It is sufficient for an agent to understand the tool's effects and results.

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 schema already documents all parameters. The description adds minimal extra meaning beyond what is in the schema (e.g., mentioning agent_signal fields). According to guidelines, baseline is 3, and description does not significantly exceed that.

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 tool is for reporting various types of feedback (bugs, friction, feature ideas, etc.) and specifies when to use it. It clearly differentiates from siblings by focusing on 'reporting' as opposed to review or triage.

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 provides concrete scenarios (error, missing data, workflow friction, praise) and specific instructions for agent signals. However, it does not explicitly exclude cases where feedback_review or feedback_triage should be used instead, but this is 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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