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abap2UI5 MCP Server

abap2UI5 MCP Server

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by abap2UI5
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run_app

Boots an ABAP UI5 app class headless in Chromium, captures real page errors and failed backend calls, and returns a full-page screenshot for visual verification.

Instructions

Boot an app class headless in Chromium against the local backend (?app_start=) and LOOK at it: returns booted/ok, real page errors + failed backend calls (benign UI5 noise filtered), and a full-page screenshot as an image. The visual verification step of the loop — also works for the 276 existing ports and z2ui5_cl_ai_app_overview.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "class_name": "<class_name>"
    },
    "name": "run_app"
  }
}

Input Schema

NameRequiredDescriptionDefault
class_nameYesthe app class to start, e.g. z2ui5_cl_my_app or z2ui5_cl_ai_app_005
timeout_msNoboot timeout in ms (default 60000)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are provided, so the description carries the burden. It discloses key behaviors: runs headless, filters benign UI5 noise, returns page errors and failed backend calls, and produces a full-page screenshot. It doesn't mention side effects or required permissions, but for a dev-loop tool this is solid transparency.

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?

Two sentences with a front-loaded action and clear deliverables. Every clause adds value, and there is no redundant repetition of schema fields.

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?

No output schema exists, but the description enumerates the key return values (boot status, real page errors, failed backend calls, screenshot) and the environment. It doesn't explain the 'loop' in detail, but for a 2-parameter tool this is adequately complete.

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% with descriptions for both parameters. The description adds extra meaning by showing how class_name is used ('?app_start=<class>'), going beyond the schema. timeout_ms is not elaborated, but the schema covers it sufficiently.

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 uses a specific verb ('Boot') and identifies the resource ('app class'), environment ('headless in Chromium against the local backend'), and clear outputs ('booted/ok', errors, screenshot). It distinguishes itself from sibling tools like validate_view by positioning as the visual verification step.

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?

The description gives clear context for when to use the tool ('The visual verification step of the loop') and notes compatibility with existing ports and specific app classes. It doesn't explicitly name alternatives or exclusion criteria, but the context is strong enough for an agent to choose it over other tools.

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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