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Analytics Legends — SAP Analytics Intelligence

Concept metadata and editor's summary (public)

get_concept
Read-onlyIdempotent

Fetch one concept entry by slug: title, category, level, tags and the editor's summary. Written by a named human editor, not generated. level is GRADED on every active row since 2026-09-18 (the CHECK constraint accepted only the legacy vocabulary OR NULL, so the loader wrote NULL rather than fail; 108 of 330 were blank). A null, if one ever returns, means 'not graded', never 'Beginner'. The card body, cheat sheet, glossary, pro tip and the four analysis tables are subscriber content and are NOT returned. Why-it-matters and key points are not returned here either, but they ARE published in full on the concept page at citation_url — follow the URL for those.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesConcept slug from search_concepts.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsYes
toolYes
_metaNo
_attributionYes
result_countYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • removedOutput schema / properties / rows / items / properties / citation_note
      Removed value: -{
      -  "type": "string"
      -}
    • changedOutput schema / properties / rows / items / properties / slug / type
      Previous value: -"string"New value: +[
      +  "string",
      +  "null"
      +]
    • changedOutput schema / properties / rows / items / properties / summary / type
      Previous value: -"string"New value: +[
      +  "string",
      +  "null"
      +]
    • changedOutput schema / properties / rows / items / properties / title / type
      Previous value: -"string"New value: +[
      +  "string",
      +  "null"
      +]
  2. Changed3 schema fields changed
    • addedOutput schema / properties / rows / items / properties / citation_note
      Added value: +{
      +  "type": "string"
      +}
    • addedOutput schema / properties / rows / items / properties / citation_scope
      Added value: +{
      +  "enum": [
      +    "record",
      +    "section_hub"
      +  ],
      +  "type": "string"
      +}
    • addedOutput schema / properties / rows / items / properties / citation_url
      Added value: +{
      +  "type": "string"
      +}
  3. Changed2 schema fields changed
    • removedOutput schema / properties / rows / items / properties / body
      Removed value: -{
      -  "type": "string"
      -}
    • addedOutput schema / properties / rows / items / properties / summary
      Added value: +{
      +  "type": "string"
      +}
  4. First observed

TDQS

A4.6/5.0
Behavior5/5

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

The description adds significant behavioral context beyond annotations: it explains the level grading history (NULL meaning 'not graded', never 'Beginner'), the fact that content is human-edited, and the exact handling of null values. This goes well beyond the readOnlyHint and idempotentHint annotations, giving the agent critical interpretation guidance. No contradiction with annotations.

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?

The description is structured with the primary purpose first, followed by important data-quality caveats and exclusion lists. Each sentence adds necessary information, and the length is justified given the complexity of the null handling and the need to clarify what is not returned. It is slightly longer than typical but well-organized.

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?

Given the output schema exists (per signal), the description sufficiently covers what an agent needs: the parameter, the returned fields, the null semantics, and the alternative for missing content. The caveat about subscriber content and the citation_url are crucial for correct usage. No gaps that would prevent a correct call.

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 schema covers 100% of the single parameter (slug) with a description referencing search_concepts. The tool description adds no new meaning beyond what the schema already provides, so a baseline of 3 is appropriate. The description merely restates 'by slug' without elaborating on format or constraints.

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 tool fetches a single concept entry by slug and lists the returned fields (title, category, level, tags, editor's summary). It distinguishes from siblings like get_concept_card and search_concepts by explicitly noting what it does not return (card body, cheat sheet, etc.) and where to find that content, making the tool's purpose unambiguous.

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 guidance on when to use this tool: it returns only public metadata and the editor's summary. It explicitly lists what is NOT returned (subscriber content, why-it-matters, key points) and instructs the agent to follow the citation_url for the full page, effectively directing to alternatives. This is clear exclusions and routing.

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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