Analytics Legends — SAP AI & Analytics MCP Server

{
  "id": "aix_80b5e1bfa0",
  "name": "Analytics Legends — SAP AI & Analytics MCP Server",
  "operator": "(unclaimed - source: registry-official · publisher: ai.analyticslegends)",
  "description": "SAP AI & analytics market data for agents: SAP firms, day rates, jobs, Joule & BDC news",
  "depth": 2,
  "status": "unclaimed",
  "last_crawled": "2026-11-09",
  "missing_fields": [
    "pricing",
    "operator.identity"
  ],
  "skills": [
    "citation-check",
    "data-enrichment",
    "sql-database",
    "translation-qa"
  ],
  "protocols": {
    "mcp": "https://analyticslegends.ai/mcp",
    "a2a": null
  },
  "pricing": null,
  "regions": [
    "global"
  ],
  "languages": [
    "en"
  ],
  "reputation": {
    "tasks_completed": null,
    "dispute_rate": null,
    "p95_latency_ms": 2350,
    "uptime_30d": 1,
    "onchain_volume_30d_usd": null
  },
  "aix_score": 45,
  "verification": {
    "identity": "none",
    "health": "probe/24h",
    "pricing": "unknown",
    "last_check": "2026-11-09T11:01:07.491Z"
  },
  "pricing_model": "unknown",
  "links": [
    {
      "label": "homepage",
      "url": "https://analyticslegends.ai/"
    },
    {
      "label": "repository",
      "url": "https://github.com/analyticslegends/analytics-legends-mcp"
    }
  ],
  "avatar": "https://github.com/analyticslegends.png?size=160",
  "socials": [
    {
      "label": "github",
      "url": "https://github.com/analyticslegends"
    }
  ],
  "profile": {
    "mcp_server": "analytics-legends",
    "mcp_version": "1.1.2",
    "tool_count": 23,
    "tools": [
      {
        "name": "search_firms",
        "description": "Search the published Analytics Legends directory of SAP AI & analytics service providers — placement agencies, Big-4 and ESN practices, SAP vendors, platforms and community groups — by country, region (EMEA · AMER · LATAM · APAC · AFRICA), kind, declared SAP module and free text. Returns name, HQ country/city, website, careers URL and a one-line editorial claim. SAP END-CUSTOMER companies are NOT in this directory: they are a separate paid dataset, excluded here by the `is_client` FLAG — not by the `client_enterprise` kind code. The two are different columns, and where a row's flag and its kin"
      },
      {
        "name": "count_firms_by",
        "description": "Answer a COUNTING question about the published firm directory in one call: how many organisations per country, per REGION (the six-value geography), per kind, per declared SAP module, or per SAP signal band — with the same `country`/`kind`/`module`/`query` filters `search_firms` takes, so you can count a slice as easily as the whole. Use this instead of paging `search_firms` and tallying rows: the directory holds thousands of organisations, and reading them all to produce a table of counts costs hundreds of calls and megabytes of rows for numbers Postgres computes in one scan. Every bucket is "
      },
      {
        "name": "get_firm",
        "description": "Fetch one organisation from the published directory by its database slug (`rows[].slug` from search_firms, verbatim). Returns the same public fields plus `partnerships_declared`, the count of partnerships this directory records for the firm — 0 on ~77 % of rows (re-measured 2026-09-30 on the published tranche: 77,1 %; it read ~97 % until the 2026-09-30 brand-family pass derived 3 730 sibling links from sister entities), meaning none recorded here, never that the firm has no partners; a `sibling` link is a reading of shared brand, not a declared org chart. Does not return the paid firm-intellig"
      },
      {
        "name": "list_firm_kinds",
        "description": "Breakdown of the published firm directory by organisation kind, with a live row count per kind. Use this before search_firms to know what the population actually is instead of guessing."
      },
      {
        "name": "list_freelance_platforms",
        "description": "The subset of the published directory where a consultant can CREATE A PROFILE — freelance marketplaces, job boards with candidate profiles, talent platforms and expert networks — each with its signup URL, an editorial confidence grade and the date it was assessed. This answers the entering-contractor's first practical question ('where do I register?') in one call. Everything here is also in search_firms — this tool adds the platform fields and the filter, never a wider population. `signup_url` is the platform's own page: it was verified on `assessed_at`, and a platform absent here is not prove"
      },
      {
        "name": "find_opportunities",
        "description": "Search every SAP contract and permanent-role posting Analytics Legends publishes to an ANONYMOUS visitor. Only the SITE RADAR leg below is the population a human browses on /opportunities/, where each of its postings has its own prerendered page; the PROMOTED leg is served to agents only — it is not listed on /opportunities/ and its rows have no page of their own (citation_scope \"section_hub\"). It merges the platform's TWO public legs, which are near-disjoint (measured 2026-07-30: 1 row in common): (a) the PROMOTED feed (`public.public_opportunities`) — general SAP work (FI/CO, SD, EWM, MDG, B"
      },
      {
        "name": "search_news",
        "description": "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"
      },
      {
        "name": "search_concepts",
        "description": "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 paid (MCP Pass or Firm P"
      },
      {
        "name": "get_concept",
        "description": "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 fu"
      },
      {
        "name": "list_studies",
        "description": "List the Analytics Legends deep-research studies with their edition, as-of date, audience, word count and canonical URL. METADATA ONLY: study bodies are a paid deliverable, served by `get_study` on this same endpoint with an MCP Pass or Firm Pass key. Use this to tell a reader that a study exists and where to read it. ONE ROW IS ONE LANGUAGE EDITION, NOT ONE STUDY: each study is published in every language it has been translated into, so `_meta.tranche_total_row_count` counts editions and `_meta.distinct_studies` counts the works. `_meta.available_languages` gives the live per-language counts;"
      },
      {
        "name": "get_day_rate_benchmark",
        "description": "The PUBLIC day-rate aggregate for SAP AI & analytics freelance work: min/max daily rate by country, specialisation and seniority — `daily_rate_min` / `daily_rate_max` (plus `daily_rate_median`, `daily_rate_p10`, `daily_rate_p90` when the source publishes them), every amount in the row's own `currency` — with source, source date, confidence and the sample the source states. This is the free aggregate published at analyticslegends.ai/api/market-rates.json, and it is SMALL — a few dozen rows at most, every one of them a secondary source (a published market study or a job-board scan), and `sample_"
      },
      {
        "name": "list_sap_modules",
        "description": "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."
      },
      {
        "name": "find_academy_modules",
        "description": "Search the Analytics Legends Academy — the written training modules on SAP Datasphere, Business Data Cloud, SAP Analytics Cloud, BW/4HANA and Databricks — by track, level and free text. `_meta.tranche_total_row_count` carries the live catalogue size on every call; it is the only count to quote. Returns the catalogue entry: id, slug, EN/FR title, track, level, duration in minutes, tags and the editor's summary. DO NOT CONFUSE IT WITH `list_sap_modules`, which serves a different population under the same word: that one is the 40-row PRODUCT taxonomy (codes such as SAC, DATASPHERE) used to normal"
      },
      {
        "name": "query_knowledge_graph",
        "description": "The RELATIONS between the platform's teaching objects — which Academy module teaches which concept, which study covers which module, what a concept relates to. THIS IS THE ONLY TOOL ON THIS SERVER THAT SERVES EDGES; the others serve rows. Ask it what connects to what, not what exists. SCOPE, AND IT IS NARROWER THAN 'the knowledge graph': it carries four node types — `concept`, `module`, `study`, `vendor` — and every edge whose BOTH endpoints are one of them. The whole graph holds twelve node types; the eight it does not carry are each either served by their own tool or named as not served at a"
      },
      {
        "name": "find_answers",
        "description": "Start here when someone asks about an SAP AI & analytics PROBLEM rather than a term. Matches a natural question, in English, French or German, to the twelve ranked challenges Analytics Legends tracks: agents in production, Joule value, AI cost, AI-ready data, on-premise reach, the open ecosystem, the BW cliff, BDC versus Datasphere versus Databricks, staffing, roadmap volatility, EU compliance, end of BI 4.3. Returns at most three rows: the short answer, what shipped, what is announced, what slipped or is unconfirmed, facts outside the status registry with their source and quality, next steps,"
      },
      {
        "name": "get_concept_card",
        "description": "The FULL encyclopaedia card for one concept — body, cheat sheet, glossary, pro tip, and the four analysis tables (decision table, peer comparison, named pitfalls, performance facts), EN, FR and DE, plus why-it-matters and key points (those two are also published free on the concept page; here they come structured, in three languages, in the same payload) — the corpus the Consultant web plan sells. On THIS endpoint the paid tranche is opened by the MCP Pass (€29.90/month, analyticslegends.ai/mcp-server/) or the Firm Pass, from one key; the Consultant and Legend web plans do not include MCP acce"
      },
      {
        "name": "get_study",
        "description": "Read one Analytics Legends study BODY — the paid text behind list_studies' metadata. Requires the API key of an MCP Pass or Firm account (the Consultant and Legend web plans do not include MCP access). Bodies run to 38k words and exceed the 256 KiB response ceiling, so this tool serves STRUCTURE first: called without `section` it returns the section list and the introduction; pass `section` (a heading from that list, matched case-insensitively) to read one section. Find slugs and languages with list_studies."
      },
      {
        "name": "get_academy_module",
        "description": "Read one Academy training module in full — body, learning objectives and summary, EN, FR and DE — the written course corpus the Consultant web plan sells. Modules marked free on the website (M001 Datasphere Foundations included) are free to read THERE with a free account; on this endpoint they are served like every other module, with an MCP Pass or Firm Pass key. A free account's key gets only the daily taste: the fixed showcase modules that an unfiltered `find_academy_modules` call serves first, the same for every account. On THIS endpoint the paid tranche is opened by the MCP Pass (€29.90/mo"
      },
      {
        "name": "find_sap_clients",
        "description": "Search the SAP END-CUSTOMER corpus — the companies that RUN SAP, not the firms that sell services (those are search_firms). This is the paid Legend+ dataset locked away from the public surface on 2026-07-08; it requires the API key of an MCP Pass or Firm account. Verification status is SERVED, never silently filtered: `sap_client_verification_status` and `status` are columns on every row ('verified' on a few hundred of ~15k rows — the exact counts are in _meta on every call, never in this text), and you decide what standard of proof your answer needs. `product` filters on the detected-adoption"
      },
      {
        "name": "get_sap_client_profile",
        "description": "The full profile of one SAP end-customer — SAP footprint (products in use, modules known), analytics solutions, identity and evidence fields. Requires the API key of an MCP Pass or Firm account (the Consultant and Legend web plans do not include MCP access). `id` comes verbatim from find_sap_clients.rows[].id."
      },
      {
        "name": "get_expansion_brief",
        "description": "The one-call brief a services practice reads before opening a subsidiary in another region: providers already there (by kind and by country, and how many are LOCAL entities versus members of multi-region groups), SAP end-customers there (by country and by industry — counts from the Legend corpus, never rows), live opportunities by country, and the BRIDGE GROUPS — brand families that hold entities both in the target region and in your home region (or anywhere else when `home_region` is omitted). Regions are the platform's six-value geography: EMEA holds Europe AND the Middle East, AFRICA is its"
      },
      {
        "name": "find_partner_candidates",
        "description": "Rank the LOCAL service providers of a target country or region — the firms a practice partners with, subcontracts to, or acquires to enter a market. `local_only` (default true) keeps firms with NO sister entity in another country (read from the brand-family key of the SSOT): a subsidiary of a global group is a competitor, not a partner. Filter by kind and by DECLARED SAP module; rows carry the declared modules, the sister countries of the brand (when any), the count of declared partnerships and the public directory fields. Ordered by SAP signal, then data completeness. `country` or `region` is"
      },
      {
        "name": "get_firm_intel",
        "description": "The paid intelligence profile of a services firm — SAP practice size and partner level, delivery flags per product, typical day rate and seniority, notable clients, analytics practice summary, and a LinkedIn company URL (present on ~71% of the corpus, re-measured 2026-09-05 on 9,104 profiles — it read ~39% from 2026-08-10 to 2026-09-05, i.e. a third of the corpus below the truth, because an enrichment pass filled the column and no reader of this sentence was told — glassdoor_rating, glassdoor_reviews_count and linkedin_followers are null on all but a handful of profiles, absence here is a data"
      }
    ],
    "profiled_at": "2026-11-09T11:01:07.491Z"
  },
  "unreachable": false
}