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Format ABAP source

format_abap
Read-onlyIdempotent

Normalize ABAP source code formatting by applying abaplint's formatter to fix inconsistent keyword casing and indentation before review or commit.

Instructions

Pretty-print one ABAP source: normalize keyword casing and indentation using abaplint's formatter — the offline equivalent of Pretty Printer in ADT/SE80. Use this when generated or hand-written ABAP has inconsistent casing/indentation and you want it normalized before review or commit. It does not reformat CDS views or behavior definitions, does not change any logic, and fails cleanly on source it cannot parse. Example: format_abap({ "source": "report ztest.\nwrite 'hi'." }).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceYesThe complete ABAP source to format.
filenameNoabapGit-style name if known, e.g. "zcl_x.clas.abap"; inferred from the source when omitted.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
formattedYesThe pretty-printed source.
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds useful context beyond that: it does not change any logic, fails cleanly on unparseable source, and is an offline equivalent of Pretty Printer in ADT/SE80. This extra behavioral detail goes beyond the 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 three sentences, each earning its place: purpose, usage guidance with exclusions, and a concrete example. It is front-loaded with the core purpose and contains no filler or redundant phrases.

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?

For a moderately simple tool with an output schema and good annotations, the description is complete. It covers what the tool does, when to use it, what it deliberately does not do, failure behavior, and includes an example. The sibling tools are clearly distinguishable.

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?

The input schema already provides rich descriptions for both parameters (source and filename), including filename inference behavior. The description's example demonstrates the 'source' parameter but does not add new semantics for 'filename' beyond what the schema already states. With 100% schema coverage, the baseline 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 clearly states the verb+resource: 'Pretty-print one ABAP source' with specific details about normalizing keyword casing and indentation. It also differentiates from siblings by noting it is the offline equivalent of ADT/SE80 Pretty Printer and explicitly excludes CDS views and behavior definitions.

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?

Explicit usage guidance is provided: 'Use this when generated or hand-written ABAP has inconsistent casing/indentation and you want it normalized before review or commit.' It also lists exclusions (CDS views, behavior definitions, logic changes) and gives a concrete example, making the intended use very clear.

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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