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brand_feedback

Submit feedback to improve brand tools by reporting bugs, workflow friction, feature requests, data quality issues, or praise. Use for errors, missing data, difficult workflows, or positive experiences.

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.
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: feedback is 'Stored locally in ~/.brandsystem/feedback/ for developer triage' and 'Returns a feedback ID.' It covers storage location, purpose (developer triage), and return value, though it doesn't mention permissions, rate limits, or error handling.

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?

The description is efficiently structured with three sentences that each add value: first states purpose, second gives usage guidelines, third explains storage and return. There's no wasted text, and key information is front-loaded appropriately.

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?

For a tool with 11 parameters, no annotations, and no output schema, the description provides good context about usage, storage, and return value. It could be more complete by explaining error cases or response formats, but it covers the essential behavioral aspects given the complexity.

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 11 parameters thoroughly. The description adds minimal parameter semantics beyond the schema, mainly clarifying the 'agent_signal' category usage. This meets the baseline of 3 when schema coverage is high.

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 tool's purpose: 'Report bugs, friction, feature ideas, data quality issues, praise, or structured agent signals to the brandsystem team.' It specifies the verb ('Report') and resource ('to the brandsystem team'), and distinguishes itself from sibling tools by being the only feedback submission tool in the list.

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

Usage Guidelines5/5

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

The description provides explicit usage guidance: 'Use when a tool returns an error, extraction misses data, the workflow feels harder than it should, or something works particularly well.' It also gives specific instructions for structured agent telemetry with category='agent_signal', making it clear when and how to use this tool versus alternatives.

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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