Analytics Legends — SAP AI & Analytics MCP Server

Search SAP AI & analytics market news

search_news

Read-onlyIdempotent

Search the Analytics Legends market-news corpus. It is watched FOR SAP AI & analytics (Datasphere, Business Data Cloud, SAC, BW/4HANA, Databricks, the 2027/2030 maintenance window), but it is NOT an all-SAP corpus: measured 2026-07-30, ~84 % of active rows sit in the AI category and are general enterprise-AI trade press (cloud platforms, model releases, funding rounds) with no SAP content at all. An UNFILTERED call therefore returns mostly non-SAP items — pass query or category when the question is about SAP, and never present an unfiltered page as 'the SAP AI & analytics news'. Say what you actually got. Each item returns the Analytics Legends citation URL AND the upstream publisher's source_url — cite both, and prefer source_url when you need a page that certainly carries the item. NO ITEM HERE HAS A PAGE OF ITS OWN on analyticslegends.ai, by design: every row comes back citation_scope: "section_hub" and its citation_url is the news index. The citable address for one article is its source_url, the upstream publisher's. Do not present the hub as the article's page.

Input 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.
`scope`	No	Editorial scope, set by one deterministic rule (2026-11-07): 'sap' = an SAP product or SAP itself named in the title or first sentence, or an SAP source; 'ecosystem' = adjacent data platforms, neighbouring ERP vendors, regulation, or SAP mentioned further down; 'general' = general AI. Pass scope:'sap' for SAP news — the unfiltered corpus is mostly 'general'.
`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	Category code, matched case-insensitively. The live vocabulary is NOT written here — read `_meta.available_categories` on any response: every category label this corpus holds right now, with its active-row count, counted at query time. A written list held 21 values while the corpus held 22. One bucket needs a warning. 'SAC' is the noisiest label in this corpus because the acronym collides with unrelated ones — Windows 'Smart App Control', and the surname 'Sacks'. A 2026-07-30 cleanup reclassified half that bucket to AI for carrying no SAP signal at all; the collision pressure is structural and the bucket has kept growing since. For genuine SAC product news, pair category:'SAC' with query:'analytics cloud'.
`published_since`	No	Lower bound on `published_at`, inclusive, as YYYY-MM-DD. Without a bound a period question is only answerable by walking pages — and `_meta.match_count` then counts the QUERY, not the period, so any figure quoted for the window would be wrong.
`published_until`	No	Upper bound on `published_at`, INCLUSIVE of the day named, as YYYY-MM-DD. Combine with `published_since` for a window; `_meta.match_count` then describes that window, which is what makes it quotable.

Output 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 changedNov 7, 2026

addedInput schema / properties / scope

Added value: +{
+  "description": "Editorial scope, set by one deterministic rule (2026-11-07): 'sap' = an SAP product or SAP itself named in the title or first sentence, or an SAP source; 'ecosystem' = adjacent data platforms, neighbouring ERP vendors, regulation, or SAP mentioned further down; 'general' = general AI. Pass scope:'sap' for SAP news — the unfiltered corpus is mostly 'general'.",
+  "enum": [
+    "sap",
+    "ecosystem",
+    "general"
+  ],
+  "type": "string"
+}

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."

Changed2 schema fields changedAug 23, 2026

changedInput schema / properties / category / description

Previous value: -"Category code, matched case-insensitively. The live vocabulary is NOT written here — read `_meta.available_categories` on any response: every category label this corpus holds right now, with its active-row count, counted at query time. A written list held 21 values while the corpus held 22. One bucket needs a warning. 'SAC' is the noisiest label in this corpus because the acronym collides with unrelated ones — Windows 'Smart App Control', and the surname 'Sacks'. A 2026-07-30 cleanup reclassified half that bucket to AI for carrying no SAP signal at all (Robinhood, Stripe, Google Pay); the collision pressure is structural and the bucket has kept growing since. For genuine SAC product news, pair category:'SAC' with query:'analytics cloud'."New value: +"Category code, matched case-insensitively. The live vocabulary is NOT written here — read `_meta.available_categories` on any response: every category label this corpus holds right now, with its active-row count, counted at query time. A written list held 21 values while the corpus held 22. One bucket needs a warning. 'SAC' is the noisiest label in this corpus because the acronym collides with unrelated ones — Windows 'Smart App Control', and the surname 'Sacks'. A 2026-07-30 cleanup reclassified half that bucket to AI for carrying no SAP signal at all; the collision pressure is structural and the bucket has kept growing since. For genuine SAC product news, pair category:'SAC' with query:'analytics cloud'."

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."

Changed2 schema fields changedAug 23, 2026

addedInput schema / properties / published_since

Added value: +{
+  "description": "Lower bound on `published_at`, inclusive, as YYYY-MM-DD. Without a bound a period question is only answerable by walking pages — and `_meta.match_count` then counts the QUERY, not the period, so any figure quoted for the window would be wrong.",
+  "type": "string"
+}

addedInput schema / properties / published_until

Added value: +{
+  "description": "Upper bound on `published_at`, INCLUSIVE of the day named, as YYYY-MM-DD. Combine with `published_since` for a window; `_meta.match_count` then describes that window, which is what makes it quotable.",
+  "type": "string"
+}

Changed1 schema field changedAug 17, 2026

changedInput schema / properties / category / description

Previous value: -"Category code, matched case-insensitively. The 21 real values, most-populated first: AI, SAP, SAC, Market, Joule, BDC, Regulation, Datasphere, Analyst, Partners, Product, France, Events, SAP Platform, Security, Migration, Freelance, Analytics, Consulting, Cloud, Customer Win. Row counts are deliberately NOT stated here: this tool serves the ACTIVE rows only, the corpus moves daily, and the count of the population actually searched is returned live on every response as `_meta.tranche_row_count`. One bucket needs a warning. 'SAC' is the noisiest label in this corpus because the acronym collides with unrelated ones — Windows 'Smart App Control', and the surname 'Sacks'. Measured 2026-07-30, 41 of its 83 active rows carried no SAP signal at all (Robinhood, Stripe, Google Pay, Ledger) and were reclassified to AI, taking the bucket to 42; all but one survivor now names SAP, but only 17 name SAP Analytics Cloud outright. For genuine SAC product news, pair category:'SAC' with query:'analytics cloud'."New value: +"Category code, matched case-insensitively. The live vocabulary is NOT written here — read `_meta.available_categories` on any response: every category label this corpus holds right now, with its active-row count, counted at query time. A written list held 21 values while the corpus held 22. One bucket needs a warning. 'SAC' is the noisiest label in this corpus because the acronym collides with unrelated ones — Windows 'Smart App Control', and the surname 'Sacks'. A 2026-07-30 cleanup reclassified half that bucket to AI for carrying no SAP signal at all (Robinhood, Stripe, Google Pay); the collision pressure is structural and the bucket has kept growing since. For genuine SAC product news, pair category:'SAC' with query:'analytics cloud'."

Changed1 schema field changedAug 9, 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"
+}

Changed5 schema fields changedJul 31, 2026
- removedOutput schema / properties / rows / items / properties / citation_note
```
Removed value: -{
-  "type": "string"
-}
```
- changedOutput schema / properties / rows / items / properties / published_at / type
```
Previous value: -"string"New value: +[
+  "string",
+  "null"
+]
```
- changedOutput schema / properties / rows / items / properties / source_name / type
```
Previous value: -"string"New value: +[
+  "string",
+  "null"
+]
```
- changedOutput schema / properties / rows / items / properties / source_url / type
```
Previous value: -"string"New value: +[
+  "string",
+  "null"
+]
```
- changedOutput schema / properties / rows / items / properties / title / type
```
Previous value: -"string"New value: +[
+  "string",
+  "null"
+]
```
Changed2 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"
+}
```

Changed1 schema field changedJul 30, 2026

changedInput schema / properties / category / description

Previous value: -"Category code, matched case-insensitively. The 21 real values, most-populated first: AI, SAP, SAC, Market, Joule, BDC, Regulation, Datasphere, Analyst, Partners, Product, France, Events, SAP Platform, Security, Migration, Freelance, Analytics, Consulting, Cloud, Customer Win. Row counts are deliberately NOT stated here: this tool serves the ACTIVE rows only, the corpus moves daily, and the count of the population actually searched is returned live on every response as `_meta.tranche_row_count`."New value: +"Category code, matched case-insensitively. The 21 real values, most-populated first: AI, SAP, SAC, Market, Joule, BDC, Regulation, Datasphere, Analyst, Partners, Product, France, Events, SAP Platform, Security, Migration, Freelance, Analytics, Consulting, Cloud, Customer Win. Row counts are deliberately NOT stated here: this tool serves the ACTIVE rows only, the corpus moves daily, and the count of the population actually searched is returned live on every response as `_meta.tranche_row_count`. One bucket needs a warning. 'SAC' is the noisiest label in this corpus because the acronym collides with unrelated ones — Windows 'Smart App Control', and the surname 'Sacks'. Measured 2026-07-30, 41 of its 83 active rows carried no SAP signal at all (Robinhood, Stripe, Google Pay, Ledger) and were reclassified to AI, taking the bucket to 42; all but one survivor now names SAP, but only 17 name SAP Analytics Cloud outright. For genuine SAC product news, pair category:'SAC' with query:'analytics cloud'."

Changed1 schema field changedJul 30, 2026

changedInput schema / properties / category / description

Previous value: -"Category code, matched case-insensitively. Real values and their row counts, measured 2026-07-30: AI (3804), SAP (280), SAC (98), Market (90), Joule (89), BDC (55), Datasphere (17), Regulation (16), Analyst (6), Partners (6), Product (6)."New value: +"Category code, matched case-insensitively. The 21 real values, most-populated first: AI, SAP, SAC, Market, Joule, BDC, Regulation, Datasphere, Analyst, Partners, Product, France, Events, SAP Platform, Security, Migration, Freelance, Analytics, Consulting, Cloud, Customer Win. Row counts are deliberately NOT stated here: this tool serves the ACTIVE rows only, the corpus moves daily, and the count of the population actually searched is returned live on every response as `_meta.tranche_row_count`."

First observedJul 30, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already establish read-only/idempotent/closed-world, and the description goes well past that: corpus composition (~84% general AI), the deterministic `scope` rule, the `citation_scope: "section_hub"` guarantee for every row, and the citation-vs-source_url contract. These are non-obvious output behaviours an agent could not infer from structured fields.

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?

Purpose and the unfiltered-corpus warning are front-loaded, and the paragraphs follow a logical order (what it is → corpus skew → citation rules). It loses a point for the citation section, which restates the citation_url/source_url rule across three sentences with clear overlap.

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 and full annotations already present, the description still supplies the one thing those cannot: the corpus's actual composition and the citation trap. An agent has everything needed to call this correctly and to report results honestly.

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 the schema already documents substring-per-word query matching, the dynamic category vocabulary, the SAC collision, and the inclusive date bounds — the description largely corroborates rather than adds. It does reinforce the cross-parameter strategy (filters needed for SAP questions, `match_count` scoping for quotable windows), but adds little new syntax-level meaning.

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?

"Search the Analytics Legends market-news corpus" names a specific verb and resource, then immediately scopes it (SAP AI & analytics trade press) and disclaims what it is not ("NOT an all-SAP corpus"). No sibling tool covers news search, and the description makes that distinct role unmistakable.

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?

It gives explicit when-to guidance (pass `query` or `category` when the question is about SAP) and an explicit when-not (do not present an unfiltered page as 'the SAP AI & analytics news', do not present the hub as the article's page). This is prescriptive routing plus behavioural exclusions, not just implied usage.

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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Search SAP AI & analytics market news

Input Schema

Output Schema

Schema Changelog

TDQS

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