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Search the bundled SAP knowledge base

search_sap_knowledge
Read-onlyIdempotent

Search a curated knowledge bundle for SAP fact questions about ABAP Cloud release deltas, Clean Core governance, and SAP AI options. Returns cited sources, confidence flags, and full decision cards.

Instructions

Search one dated, cited knowledge bundle covering three areas: ABAP Cloud / RAP release deltas, Clean Core governance (SAP's extensibility Levels A-D, release contracts C0 to C4, the real ATC check and variant names, and the SAP/abap-atc-cr-cv-s4hc Cloudification Repository file and schema inventory), and decision cards for the SAP AI options an ABAP team can reach (SAP-ABAP-1, the Generative AI Hub orchestration API, the ABAP AI SDK powered by ISLM, Joule for Developers, SAP's official ADT MCP server, the custom code migration agent, and the community MCP ecosystem). Every hit returns its sources, a confidence flag and the full card. Use this when a question is about SAP facts rather than about a piece of source text — which Clean Core level a pattern lands in, what a C1 release contract guarantees, which ATC variant to run, whether SAP-ABAP-1 accepts a system prompt, which classes the ABAP AI SDK exposes, or what SAP's own MCP server can and cannot do. It does not connect to any SAP system, does not run ATC, does not fetch anything live, and is not a substitute for a real ATC run or for SAP's own documentation — it is a curated snapshot (curated 2026-09-10) and the target system and SAP's current documentation stay authoritative. For release-timeline questions prefer explain_abap_release; for the release state of a specific object use check_released_api; to analyze actual source text use lint_abap or check_cloud_readiness. Example: search_sap_knowledge({ "query": "sap-abap-1 system prompt" }).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
areaNoRestrict the search: "release" (release deltas), "clean-core" (levels, contracts, ATC and repository facts), "sap-ai" (the AI decision cards), or "all" (default).
limitNoMaximum ranked cards to return, 1 to 25; defaults to 5. The SAP-AI cards are large, so keep this small.
queryYesThe question or terms to search for, e.g. "clean core level C", "release contract C1", "sap-abap-1 system prompt", "ADT MCP server tools". Names, identifiers and class names work well.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
hitsYesRanked cards, most relevant first.
scopeNoteYesDated-knowledge caveat to repeat to the user.
truncatedYesTrue when more cards matched than were returned.
matchCountYesHow many cards matched before the limit was applied.
curatedDateYesDate the bundled knowledge base was curated.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.12.0

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint and openWorldHint=false, but the description goes well beyond them: it states it does not connect to any SAP system, does not run ATC, fetches nothing live, is a curated snapshot dated 2026-09-10, and that the target system and SAP docs remain authoritative. It also discloses the return shape (sources, confidence flag, full card). This is exactly the extra behavioral context annotations cannot carry.

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?

Front-loaded with purpose, then usage, then exclusions, then an example call — the ordering is good and every sentence does work. The long parenthetical enumerations of card names and ATC artifacts are dense and a little bulky, which costs it a point, but they serve the routing job.

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 multi-area search tool with three params, full schema coverage, an output schema and rich annotations, the description covers scope, freshness, authority limits, exclusions and alternatives. Nothing needed to select or invoke it correctly is missing.

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 area, limit and query are already fully documented in the schema, including the enum values and the 1-25 bound. The description adds no parameter-specific syntax or defaults beyond that, so the baseline 3 applies.

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?

States a specific verb and resource (search a dated, cited SAP knowledge bundle) and enumerates the three covered areas in enough detail that an agent knows the answer surface without opening the schema. It also explicitly distinguishes itself from factual-authority siblings by framing the tool as a curated snapshot rather than a live system.

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?

Gives an explicit usage trigger ('when a question is about SAP facts rather than about a piece of source text'), concrete example questions, and explicit negative routing to four alternatives: explain_abap_release, check_released_api, lint_abap, and check_cloud_readiness. Both the when and the when-not are stated.

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