Analytics Legends — SAP Analytics Intelligence

SAP AI & analytics module taxonomy

list_sap_modules

Read-onlyIdempotent

The canonical SAP module/product taxonomy Analytics Legends classifies against (codes and EN/FR labels by category). Use it to normalise a user's loose product wording — 'SAC', 'Analytics Cloud', 'Datasphere' — onto the codes the other tools filter on.

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.

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

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"
+}

Changed4 schema fields changedJul 31, 2026
- changedOutput schema / properties / rows / items / properties / category / type
```
Previous value: -"string"New value: +[
+  "string",
+  "null"
+]
```
- removedOutput schema / properties / rows / items / properties / citation_note
```
Removed value: -{
-  "type": "string"
-}
```
- changedOutput schema / properties / rows / items / properties / code / type
```
Previous value: -"string"New value: +[
+  "string",
+  "null"
+]
```
- changedOutput schema / properties / rows / items / properties / label_en / 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

A4.2/5.0

Behavior4/5

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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, destructiveHint=false, closed-world), so the bar is lower. The description still adds useful content semantics — bilingual EN/FR labels organised by category and the canonical code set — that the annotations do not convey.

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?

Two sentences, front-loaded with what the resource is before explaining why to call it. No filler text; every clause carries information.

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?

An output schema exists, so return-value detail is not required, and the parameters are self-documenting. Purpose and use case are covered; the only minor omission is any note about pagination behaviour across pages, which the cursor description largely handles.

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 limit, query and cursor are fully documented in the schema itself, including the substring-per-word matching rule and cursor reuse constraints. The description adds no additional parameter meaning, 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 ('canonical SAP module/product taxonomy') and specifies what it contains ('codes and EN/FR labels by category'), which is more precise than the name alone. It also positions itself relative to the other tools by noting these are 'the codes the other tools filter on', so an agent can tell it apart from look-alikes such as list_firm_kinds and list_freelance_platforms.

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

Usage Guidelines4/5

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

Gives an explicit when-to-use with concrete examples ('SAC', 'Analytics Cloud', 'Datasphere') and the goal of normalising loose wording onto filter codes. There is no when-not guidance or named alternative tool, which keeps it short of a 5.

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

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