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GetAdtTypes

Retrieve all valid ADT object types or validate a specific type name to confirm supported SAP ADT types before building requests.

Instructions

[read-only] Retrieve all valid ADT object types (CLAS, TABL, PROG, DEVC, FUGR, INTF, DDLS, DTEL, DOMA, SRVD, SRVB, BDEF, DDLX, etc.) or validate a specific type name.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
detailNoHow much of the answer to return: "terse" (default, the fields you need to act), "full" (the whole parse), "raw" (the document as ADT sent it).terse
validate_typeNoType name to validate (optional)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv16.0.0
    • addedInput schema / properties / detail
      Added value: +{
      +  "default": "terse",
      +  "description": "How much of the answer to return: \"terse\" (default, the fields you need to act), \"full\" (the whole parse), \"raw\" (the document as ADT sent it).",
      +  "enum": [
      +    "terse",
      +    "full",
      +    "raw"
      +  ],
      +  "type": "string"
      +}
  2. First observed

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are supplied, so the description carries the behavioral burden; it declares the operation read-only via the '[read-only]' prefix, which is genuinely useful. Beyond that it says nothing about return format, validation-failure behavior, or error semantics, leaving the read-only marker as its main behavioral contribution.

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?

A single front-loaded sentence with no filler; the parenthetical example list is long but earns its place by supplying valid domain values. Slightly dense, but nothing is wasted.

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?

For a two-parameter, zero-required lookup tool with no output schema, the description conveys both modes (list-all and validate-one) and the resource being returned. It does not describe the shape of a validation result, a minor gap given the absent output schema.

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 enum for 'detail' is fully documented and the baseline is 3. The description goes beyond the schema by listing actual valid type codes (CLAS, TABL, PROG, DDLS, ...), which materially helps an agent populate 'validate_type' with a legal value.

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?

States a specific verb (Retrieve) and resource (ADT object types) and enumerates concrete type codes, so the agent knows exactly what the tool returns. It does not explicitly differentiate itself from the similarly named sibling GetTypeInfo, but the scope ('all valid ADT object types') is unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The 'or validate a specific type name' clause implies the second mode of use, but the description never states when to prefer this over GetTypeInfo, GetObjectsByType, or any Check* tool. Usage is inferable from the phrasing rather than spelled out.

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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