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Brand-System

@brandsystem/mcp

Official

brand_feedback

Send feedback signals to the brandsystem team about tool errors, friction, missing features, data quality issues, or positive experiences to drive workflow improvements.

Instructions

Send a feedback signal to the brandsystem team about a tool or workflow. Use when a tool errors, extraction misses obvious data, the workflow felt harder than it should, an agent's path got blocked, or something worked particularly well and should be preserved. Set category to one of: 'bug' (broken), 'friction' (works but painful), 'feature_request' (capability missing), 'data_quality' (results wrong/incomplete), 'praise' (worth keeping), 'agent_signal' (structured telemetry — also pass signal + tool_used + signal_context; brand context auto-fills from .brand/config). Provide a one-line summary plus optional detail/severity/context. Writes to ~/.brandsystem/feedback/ AND attempts a fire-and-forget POST to brandcode.studio (drains backlog if previously offline). Returns the feedback ID and remote-send status. NOT for reading existing feedback — use brand_feedback_review. NOT for changing item status — use brand_feedback_triage.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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.
signalNoSignal type. Required when category is 'agent_signal'. 'positive': tool worked well, 'negative': tool failed or gave poor results, 'suggestion': improvement idea.
contextNoOptional structured context about the session.
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.
outcomeNoWhat happened as a result. Optional for positive signals.
summaryYesOne-line summary of the feedback.
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).
severityNoHow much this impacts the agent's ability to serve the user. Defaults to 'suggestion'.
tool_nameNoWhich brandsystem tool this feedback relates to (e.g. 'brand_extract_web', 'brand_compile'). Optional for general feedback.
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'.
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. It discloses side effects (writes to local file, attempts remote POST, drains backlog), return value (feedback ID and remote-send status), and conditional logic (agent_signal requirements). It does not mention auth or rate limits, but auto-fill from .brand/config is noted. Slight gap on permission needs, but overall thorough.

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 well-structured: purpose, when-to-use, parameter guidelines, side effects, return value, and exclusions. Each sentence serves a distinct purpose, no fluff. Information is front-loaded with the core action.

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 the complexity (11 parameters, nested objects, conditional logic, no output schema), the description covers use cases, conditional requirements, side effects, return value, and exclusions comprehensively. It also mentions auto-fill behavior, making it highly complete for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds significant value by explaining category enum meanings, conditional requirements for agent_signal, alias behavior of message/detail, and auto-fill of brand context. This deepens understanding beyond schema alone.

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 starts with 'Send a feedback signal to the brandsystem team about a tool or workflow,' clearly stating the verb, resource, and target. It explicitly distinguishes from sibling tools by noting 'NOT for reading existing feedback — use brand_feedback_review. NOT for changing item status — use brand_feedback_triage.'

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 when-to-use scenarios: 'Use when a tool errors, extraction misses obvious data...' and also when-not-to-use with specific alternative tool names. This gives clear context for decision-making.

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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