Skip to main content
Glama

Assess a broken AI-built app

assess_broken_app
Read-only

An AI agent confirms a problem in a live web app for free, then fixes it in the code: $79 for one bug, paid up front. If we can't complete your re-check, you get your money back. Works with apps built with Lovable, Base44, Bolt, Replit, v0 or similar. The most common problem: 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 the next step: a free check on the live app. 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

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

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

A3.8/5.0
Behavior4/5

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

Annotations already declare this a read-only, non-destructive call, so safety is covered; the description adds genuinely non-obvious traits: it costs $79 up front, is refundable if the re-check can't complete, and returns a likely cause, confirming evidence, and a next step. The commercial framing is noise for an agent but the cost/refund/return-shape details are exactly the kind of behavior annotations cannot express.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The definition opens with a pricing pitch rather than the tool's action, burying the core capability behind $79/refund marketing. The symptom catalogue is useful but runs long, and several sentences (builder name-dropping, "most common problem") could be trimmed without losing routing information.

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?

An output schema exists, yet the description still helpfully sketches what comes back (likely cause, evidence, next step), and it covers scope, pricing, exclusions, and supported platforms. An agent has enough to decide and call correctly; only the missing sibling-level disambiguation keeps it from a 5.

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 url, symptom, and platform are already well documented with examples. The description adds no extra syntax or constraints beyond what the schema states, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The opening sentence states a specific verb and resource ("confirms a problem in a live web app... then fixes it in the code"), and the scope of symptoms (signup email loops, blank pages, unsaved payments) makes the domain concrete. It does not distinguish itself from the closest sibling check_live_app, which an agent could easily confuse with the "free check" this tool hands off to.

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?

Explicit inclusion ("Call it when someone describes a problem in a web app they built and can't debug themselves") and exclusion ("Do NOT call it for code you can read and fix directly, or for a library or framework question") give the agent real routing rules. It stops short of naming an alternative when a call is excluded, and never mentions siblings like check_live_app or confirm_fix.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.