Analytics Legends — SAP Analytics Intelligence

Search the SAP AI & analytics concept encyclopaedia

search_concepts

Read-onlyIdempotent

Search the SAP AI & analytics concept encyclopaedia — the vocabulary of the stack, written for practitioners. Returns slug, title, category, level, tags and the editor's summary. 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'. These are the same fields get_concept returns for ONE slug. The card BODY (cheat sheet, glossary, pro tip and the four analysis tables) is Consultant-tier: call get_concept_card. Why-it-matters and key points are NOT served by this endpoint either, but they are published in full on the concept page at citation_url — follow the URL for those, a plan buys the body, not them.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Max rows (hard cap 50).
`query`	No	Free-text filter, case-insensitive. EVERY word must appear in the record (substring per word, any order), so a natural-language phrase narrows the answer instead of having to match verbatim.
`cursor`	No	Opaque token from a previous response's `_meta.next_cursor`. Pass it back with the SAME filter arguments; `null` means the last page. Changing a filter refuses the cursor.
`category`	No	Concept category, matched case-insensitively as an exact value OR a prefix — so category:"datasphere" reaches 'Datasphere Core'. The values are long human labels, not codes. DO NOT GUESS THEM FROM THIS TEXT: the live vocabulary with a row count per label comes back as `_meta.available_categories` on EVERY call, including a call that matched nothing. A list written here would say 14 labels with 2026-07-30 counts; the corpus holds 15 today, and five of those counts have moved. Read the envelope, not the prose.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`rows`	Yes
`tool`	Yes
`_meta`	No
`_attribution`	Yes
`result_count`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedAug 26, 2026

changedInput schema / properties / query / description

Previous value: -"Free-text filter, matched case-insensitively."New value: +"Free-text filter, case-insensitive. EVERY word must appear in the record (substring per word, any order), so a natural-language phrase narrows the answer instead of having to match verbatim."

Changed1 schema field changedAug 23, 2026

changedInput schema / properties / cursor / description

Previous value: -"Opaque token from a previous response's `_meta.next_cursor`. Pass it back with the SAME filter arguments to read the next page; a null `next_cursor` means you have reached the end. It is bound to those filters and refused if they change — a cursor names a POSITION in one ordering, and applying it to another query would start the page in the wrong place."New value: +"Opaque token from a previous response's `_meta.next_cursor`. Pass it back with the SAME filter arguments; `null` means the last page. Changing a filter refuses the cursor."

Changed1 schema field changedAug 10, 2026

changedInput schema / properties / category / description

Previous value: -"Concept category, matched case-insensitively as an exact value OR a prefix. The 14 real values are long labels, measured 2026-07-30: Generative AI Foundations (55), Joule & SAP AI (42), Career & Practice (29), Governance & Compliance (25), Architecture & Performance (25), SAC (Analytics Cloud) (22), BDC Platform (22), Adjacent Stacks (19), Datasphere Core (17), Sales & Commercial (16), Productivity & AI (10), Industry Solutions (10), BW/4HANA & Migration (9), Data Engineering & Integration (1). So category:\"datasphere\" reaches Datasphere Core."New value: +"Concept category, matched case-insensitively as an exact value OR a prefix — so category:\"datasphere\" reaches 'Datasphere Core'. The values are long human labels, not codes. DO NOT GUESS THEM FROM THIS TEXT: the live vocabulary with a row count per label comes back as `_meta.available_categories` on EVERY call, including a call that matched nothing. A list written here would say 14 labels with 2026-07-30 counts; the corpus holds 15 today, and five of those counts have moved. Read the envelope, not the prose."

Changed1 schema field changedAug 10, 2026

addedInput schema / properties / cursor

Added value: +{
+  "description": "Opaque token from a previous response's `_meta.next_cursor`. Pass it back with the SAME filter arguments to read the next page; a null `next_cursor` means you have reached the end. It is bound to those filters and refused if they change — a cursor names a POSITION in one ordering, and applying it to another query would start the page in the wrong place.",
+  "maxLength": 512,
+  "type": "string"
+}

Changed3 schema fields changedJul 31, 2026
- 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 / title / type
```
Previous value: -"string"New value: +[
+  "string",
+  "null"
+]
```
Changed3 schema fields changedJul 31, 2026
- 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"
+}
```
First observedJul 30, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), and the description layers on genuinely non-obvious behavior: level is now graded on every active row, a null means 'not graded' and never 'Beginner', and the body/why-it-matters content is tier-gated or served elsewhere. This is behavioral context an agent cannot infer from annotations or schema.

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-loads what it returns, then constraint caveats, then sibling alternatives. Mostly efficient, but the parenthetical backstory (CHECK constraint, '108 of 330 were blank') is heavier than needed to convey that null means 'not graded'.

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?

With an output schema present, the description needn't explain return shape, yet it still covers pagination semantics, tier gating, the null-level edge case, and where the non-served fields live. Nothing an agent needs to invoke this correctly is missing.

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 coverage is 100%, so baseline is 3, but the description adds real semantic value on top: cursor must be replayed with identical filters or it is refused, null cursor means last page, and category values are long human labels discovered from _meta.available_categories rather than guessed. The category 'read the envelope, not the prose' guidance materially improves invocation.

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 (Search) and resource (SAP AI & analytics concept encyclopaedia / vocabulary of the stack), and explicitly enumerates the returned fields (slug, title, category, level, tags, editor's summary). It distinguishes itself from siblings by noting these are 'the same fields get_concept returns for ONE slug' and that the card body lives in get_concept_card.

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 explicit routing: use this for multi-record search, get_concept for one slug, get_concept_card for the Consultant-tier body, and follow citation_url for why-it-matters/key points. It also names what this endpoint explicitly does NOT serve, which prevents wrong-tool selection.

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

All Analytics Legends — SAP Analytics Intelligence tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.