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Analytics Legends — SAP Analytics Intelligence

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

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax rows (hard cap 50).
queryNoFree-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.
cursorNoOpaque 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.
categoryNoCategory 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_sinceNoLower 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_untilNoUpper 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

TableJSON Schema
NameRequiredDescriptionDefault
rowsYes
toolYes
_metaNo
_attributionYes
result_countYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • 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."
  2. Changed2 schema fields changed
    • 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."
  3. Changed2 schema fields changed
    • 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"
      +}
  4. Changed1 schema field changed
    • 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'."
  5. Changed1 schema field changed
    • 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"
      +}
  6. Changed5 schema fields changed
    • 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"
      +]
  7. Changed2 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"
      +}
  8. Changed1 schema field changed
    • 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'."
  9. Changed1 schema field changed
    • 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`."
  10. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Adds substantial context beyond the readOnly/idempotent annotations: the corpus is ~84% general enterprise-AI trade press with no SAP content, unfiltered calls return mostly non-SAP items, and every row comes back citation_scope "section_hub" with an index citation_url while the true article page is source_url. These are exactly the behavioral traits annotations cannot convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the corpus description, but the citation guidance is stated twice ("cite both ... prefer source_url" and again "The citable address for one article is its source_url ... Do not present the hub as the article's page"), which inflates length. Most sentences carry distinct warnings, but the redundancy and overall length keep it from being tight.

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 and 100% schema coverage, the description still covers the non-obvious gaps: corpus composition, filtering necessity, citation semantics, and cursor reuse. An agent has everything needed to call the tool correctly and interpret results honestly.

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 the baseline is 3, but the description adds real meaning: it explains why the category vocabulary is not written out (read _meta.available_categories) and warns that 'SAC' is structurally noisy due to acronym collisions, with a workaround (pair with query:'analytics cloud'). It also justifies why published_since/until matter for match_count correctness.

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?

Opens with a specific verb+resource ("Search the Analytics Legends market-news corpus") and immediately scopes it (SAP AI & analytics topics, not an all-SAP corpus). An agent knows exactly what this tool returns versus siblings like search_firms or search_concepts, which are entity searches rather than news retrieval.

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 when-to and when-not-to guidance: "pass `query` or `category` when the question is about SAP, and never present an unfiltered page as 'the SAP AI & analytics news'." It also states the correct pattern for cursors (pass back with the SAME filters) and for period questions (use published_since/until so match_count is quotable), and prescribes citation behavior.

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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