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loopback_submit_feedback

File a new feedback item tagged to a project, capturing UI defects, backend errors, usage insights, or UX issues with optional severity, console, network, and reproduction details.

Instructions

Create a new feedback item in the Loopback bus.

Use this to file any observation about a running product: a UI defect, a backend error, a usage insight, or a UX papercut. Every item is tagged to a project so the right agent picks it up later.

Args: project (slug), type (ui|backend|usage|ux), title, and optionally body, severity (p0-p3, default p2), source, reporter, route, url, dom_selector, screenshot_path, replay_url, console[], network[], repro_steps[].

Returns the created item as JSON, including its generated id (fb_...). New items start with status 'open'.

Example: file "Pay button dead on iOS Safari" with project='shop-web', type='ui', severity='p1', route='/checkout'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoFull URL where observed
bodyNoFull description of the feedback/observation
typeYesFeedback dimension: 'ui' (visual/design), 'backend' (errors/API), 'usage' (analytics/behavior), 'ux' (flow/experience)
extraNoFree-form context object: LLM run ids/model/trace URLs for AI features, automation run metadata, viewport, element HTML snippet, etc.
routeNoApp route where observed, e.g. '/checkout'
titleYesShort summary, e.g. 'Checkout button dead on mobile Safari'
sourceNoWhere this feedback came frommanual
consoleNoRecent console log lines relevant to the issue
networkNoRelevant network requests (url, method, status, ms)
projectYesProject/repo slug this feedback belongs to, e.g. 'appbroda-web'
reporterNoWho reported ithuman
severityNop0=critical, p1=high, p2=normal, p3=nice-to-havep2
replay_urlNoSession replay link (e.g. PostHog replay URL)
repro_stepsNoSteps to reproduce
dom_selectorNoCSS selector of the affected element
screenshot_pathNoPath or URL of a screenshot

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesFeedback id, e.g. 'fb_mabc12_3f9a1c'
urlNo
bodyYes
typeYes
extraYesFree-form captured context. Read `extra.failed_responses` for failing requests with up to 2KB of response body, and `extra.context` for LLM/automation run metadata (run_id, model, trace_url).
linksYesRepo/branch/commit/PR of the fix
routeNo
titleYes
sourceYes
statusYes
consoleYesConsole lines captured at report time
networkYesNetwork calls captured at report time
projectYes
commentsNoFull audit trail
reporterYes
severityYes
created_atYes
replay_urlNo
resolutionNo
updated_atYes
attachmentsNoFiles attached to this item. `intent` decides what you do with each: 'reference' is context for the fix and never ships; 'asset' is a deliverable — copy it from `path` to `target_path` in the repo, commit it, and record that with loopback_link_change. Read files from `path` directly rather than fetching `url`.
repro_stepsYes
dom_selectorNo
assignee_agentNoAgent currently holding the claim
screenshot_pathNo
Behavior4/5

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

Annotations carry no useful hints (all false), so the description carries the burden. It discloses that new items start with status 'open', returns a JSON with generated id, and mentions tagging to a project. It does not mention permissions or idempotency, but for a create operation the main side effect is explicit from 'Create'.

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 front-loaded with the core action, then follows with use case, parameter list, return behavior, and an example. Each section earns its place without padding; the example is especially valuable and the overall length is appropriate for a 16-parameter tool.

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 (nested objects, 16 params) and that an output schema exists, the description still adds necessary context: usage scenarios, parameter listing, return format, and a concrete example. It does not explicitly discuss idempotency or failure modes, but these are partially covered by annotations and the schema.

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%, giving a baseline of 3. The description adds a concise parameter summary and a concrete example ('Pay button dead on iOS Safari' with project='shop-web', type='ui', severity='p1', route='/checkout') that clarifies how to combine parameters and the expected value formats.

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 opens with 'Create a new feedback item in the Loopback bus', clearly using a specific verb and resource, and differentiates from siblings by focusing on creation ('file any observation'). The sentence 'Every item is tagged to a project' adds scope specificity beyond the tool name/title.

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?

It provides a clear use case ('Use this to file any observation about a running product') and enumerates feedback types, but does not explicitly name alternatives or state when not to use it (e.g., existing items should be updated rather than re-submitted). The context is clear enough to avoid obvious misuse.

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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