Skip to main content
Glama

brand_feedback_review

Review agent feedback stored locally. Filter by category (bug, friction, feature_request) or status (new, quarantined, acknowledged, fixed) to triage issues and prioritize fixes.

Instructions

Review all agent feedback stored in ~/.brandsystem/feedback/. Read-only — reads local JSON files without network access. Shows summary stats (by category, severity, status) and individual items with timestamps. Filter by category (bug, friction, feature_request, agent_signal) or status (new, quarantined, acknowledged, fixed). Quarantined items were flagged for potential prompt injection. Use to triage feedback, spot recurring issues, and prioritize fixes. NOT for submitting feedback — use brand_feedback. NOT for changing item status — use brand_feedback_triage.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filter_categoryNoFilter by category. Defaults to 'all'.
filter_statusNoFilter by status. Defaults to 'new'. Use 'quarantined' to see items flagged for potential prompt injection.
Behavior5/5

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

With no annotations, the description fully covers behavioral traits: read-only, no network access, reads local JSON files, shows summary stats and individual items, explains quarantine meaning, and filter details. No contradictions.

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?

Concise and front-loaded with purpose, then filtering details, then usage exclusions. Every sentence adds value; no redundancy.

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?

Despite no output schema, the description sufficiently explains what the tool returns (summary stats and individual items with timestamps) and covers all key aspects for a simple read-only feedback review tool.

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% for both parameters. The description adds value by listing example categories (bug, friction, feature_request, agent_signal) though the schema includes more; it also explains defaults and guides use of filter_status for quarantine items.

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 it reviews all agent feedback stored locally, is read-only, and distinguishes from siblings by noting what it is not for (submitting or triaging status changes), naming brand_feedback and brand_feedback_triage as alternatives.

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?

Describes when to use (triage, spot issues, prioritize fixes) and explicitly states when not to use with clear guidance to use brand_feedback for submission and brand_feedback_triage for status changes.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Brandcode-Studio/brandsystem-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server