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Assess a broken AI-built app

assess_broken_app
Read-only

Triage a problem in a live app built with an AI app builder (Lovable, Base44, Bolt, Replit, v0 or similar). The most common one: new people can't sign up. The confirmation email never arrives, its link opens localhost or the preview, sign-up loops back or shows an error, or Google sign-in fails. It also covers blank pages, buttons that do nothing, payments and data that won't save. Returns the most likely cause, the evidence that would confirm it, and what a fix involves. Call it when someone describes a problem in a web app they built and can't debug themselves. Do NOT call it for code you can read and fix directly, or for a library or framework question.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoThe public URL of the deployed app, if there is one. Never a private or internal address.
symptomYesWhat goes wrong, in the words the person used. "The save button does nothing", "it shows a blank white page after login".
platformNoThe builder it was made with, if known: lovable, base44, v0, bolt, replit, other.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
platformNo
next_stepYes
confidenceYes
likely_causeYes
evidence_to_confirmYes
what_a_fix_involvesNo
what_this_assessment_cannot_knowNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedOutput schema / properties / platform
      Added value: +{
      +  "type": "string"
      +}
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds genuinely useful behavior context: it produces a probable cause, confirming evidence, and a fix sketch rather than a fix itself, which sets expectations for a triage-only tool.

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 lead sentence states purpose and scope immediately, followed by examples, return shape, and the call/no-call rule. The symptom inventory is long but each item maps to a real routing decision, so it is mostly earned; trimming a few redundant examples would tighten it.

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?

With an output schema present, return values need no explanation, yet the description still summarizes what comes back. Scope, exclusions, platform coverage, and the required symptom phrasing are all present, leaving nothing an agent needs in order to call this correctly.

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 description coverage is 100%, so the baseline is 3. The description earns an extra point by enumerating the symptom families (signup email not arriving, localhost links, sign-in loops, blank pages, dead buttons, payments/data not saving) that tell the agent how to phrase the required symptom parameter.

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 names a specific verb (triage) and resource (a problem in a live app built with an AI app builder), enumerating the platforms and the concrete failure classes it covers. An agent can distinguish this from a generic debugging or code-fixing tool without opening the schema.

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 gives a clear call condition (someone describes a problem in a web app they built and can't debug themselves) and an explicit exclusion (code you can read and fix directly, library/framework questions). It stops short of naming the sibling get_fix_options as the alternative, which is the only missing piece for a 5.

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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