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Compare two ABAP versions

compare_abap
Read-onlyIdempotent

Compare before and after ABAP source to see lint findings resolved or introduced, cloud-readiness score movement, and classes/methods/forms added or removed—an objective verdict for refactors.

Instructions

Compare a BEFORE and an AFTER version of ABAP source and report what a rework actually changed: lint findings resolved and introduced (matched by content, so moved-but-unchanged code is not noise), cloud-blocker / score / A–D grade movement from the same dual-parse diff as check_cloud_readiness, and structural changes — classes, methods and FORMs added or removed. Use this when reviewing a refactor, a modernization step or an AI-generated rewrite of an existing object and you need an objective better-or-worse verdict instead of eyeballing a diff. It is not a textual diff tool (use git diff to see the edits) and it cannot judge functional equivalence — behavior can change while every number improves; it does not connect to any SAP system. Example: compare_abap({ "before": [ { "source": "REPORT zr.\nWRITE 1." } ], "after": [ { "source": "REPORT zr.\nWRITE 2." } ] }).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
afterYesThe AFTER sources — the reworked version being judged. Up to 32 files, 100k chars each.
focusNoCurated rule-pack lens: report only rules carrying this abaplint tag — "Performance" for a tuning pass, "Security" for a security sweep, "Styleguide" for Clean ABAP adherence. Parser errors always surface. Ignored with preset "syntax-only". Combine with rules to re-tune individual rules in the pack.
rulesNoabaplint rule overrides applied to both sides, e.g. { "line_length": { "length": 120 } }.
beforeYesThe BEFORE sources — the current/old version of the object(s). Up to 32 files, 100k chars each.
presetNoLint preset applied identically to both sides: "style" (default) for isolated snippets, "full" when every referenced object is provided, "syntax-only" for parser errors only.style
abapVersionNoABAP language version both sides are linted against. "v758" (default) is current on-prem; "Cloud" is ABAP Cloud.v758

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
afterYesLint and readiness numbers for the AFTER side.
beforeYesLint and readiness numbers for the BEFORE side.
resolvedYesFindings present before but gone after — improvements.
matchNoteYesHow findings were matched and what the numbers do and do not mean.
introducedYesFindings present only after — regressions to fix.
outlineChangesYes
unchangedCountYesFindings present on both sides (content-matched).
Behavior5/5

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

Annotations declare readOnlyHint and idempotentHint, and the description adds meaningful behavioral context beyond that: it explains matching by content (moved-but-unchanged code is not noise), explicitly notes that it does not connect to any SAP system, and highlights a limitation ('behavior can change while every number improves'). This is valuable disclosure beyond the structured annotations.

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 well-structured and front-loaded: it starts with the main functionality, then usage guidance, then exclusions, ending with an illustrative example. Every sentence adds necessary information without repetition or fluff, and the length is justified by the tool's complexity.

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?

The description covers purpose, usage context, limitations, and provides an example. Since the tool has an output schema, the description does not need to detail return values. The mention of reuse of check_cloud_readiness logic and the explicit exclusions round out the context, making the description complete for an agent to select and use the tool.

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 coverage is 100%, so the input schema already documents all parameters with descriptions. The description adds an example call that illustrates the structure of 'before' and 'after' arrays, but it does not provide any additional parameter semantics that are not already in the schema. Therefore, a baseline score of 3 is appropriate.

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 a specific verb and resource: 'Compare a BEFORE and an AFTER version of ABAP source' and enumerates the exact kinds of results (lint findings, cloud-blocker/score/grade movement, structural changes). It explicitly differentiates itself from a textual diff tool ('use git diff to see the edits'), making it clear what this tool does and does not do.

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?

The description provides explicit usage contexts: 'Use this when reviewing a refactor, a modernization step or an AI-generated rewrite' and gives exclusions: 'It is not a textual diff tool' and 'cannot judge functional equivalence.' It also mentions that it uses the same diff as check_cloud_readiness, linking to a sibling tool without confusion.

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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