mcp server

Analytics Legends — SAP Analytics Intelligence

AI agent for SAP analytics: firms, day rates, contract radar, news, concepts, studies

Description as published by the maintainer. Source

• version 1.0.0
• active
• document understanding
• analytics

active — Registry entry last updated 2026-07-30. Dashed tags are derived by ZBS Index from the published description, not stated by the maintainer.

What this server can do

16 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.

count_firms_by(by, kind, query, country)

Answer a COUNTING question about the published firm directory in one call: how many organisations per country, per kind, or per SAP signal band — with the same `country`/`kind`/`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 a value the directory actually stores; `value: null` is a real bucket meaning the field is unknown for those rows, and it is served rather than hidden — a country table that silently drops the rows with no country adds up to less than the population and says nothing about it. Required: by.

find_opportunities(limit, query, cursor, country, location, employment_type)

Search every SAP contract and permanent-role posting Analytics Legends publishes to an ANONYMOUS visitor — the same population a human browses on /opportunities/, where each posting has its own prerendered page. 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, BTP, ABAP), all German cities, dated (posted_at is populated on 80/80 active rows since 2026-07-31), and it carries NO rate, contract_type, currency or country_code: those fields come back null on that leg, so no rate or contract term can be read off it; (b) the SITE RADAR (`/api/contracts-lean.json`) — these carry country, category, seniority, posted_at, `employment_type` and, on most of them, `expires_at`; they are the analytics-specific ones (SAC Planning, Datasphere Technical Lead, Business Data Cloud). READ `employment_type` BEFORE CALLING THIS A CONTRACT MARKET: the radar is mostly PERMANENT roles (measured 2026-08-10: 2,172 permanent, 176 freelance, 33 contract of 2,381 active rows), so an unfiltered page answers a freelance question with salaried jobs unless you filter. The argument of the same name does the filtering. TWO DIFFERENT RATE FIELDS, AND THEY MEAN DIFFERENT THINGS. `currency` / `daily_rate_min` / `daily_rate_max` are the posting's OWN advertised rate and are almost always null (measured 2026-08-10: 147, 19 and 20 rows of 2,381) — most listings publish no rate at all. `rate_band` is the platform's editorial benchmark for that posting's (seniority × product × region) cell, present on 1,421 of 2,381 rows, and it is what the posting's public page leads with. It is `rate_basis: "panel_inferred"` — Eursap n=312 plus the Analytics Legends operator panel, permanent rows restated as a TJM equivalent at ~220 billable days a year — NOT a rate this employer offered. Quote it as a band with its `basis`, `kind` and `source`, never as the posting's rate, and never average bands across postings: many rows share one cell. WHAT IS GATED IS A FIELD, NOT A ROW: on most radar rows `source_url` is null and `application_link` reads "members_only" — the verified link to the original listing is the paid Consultant-tier deliverable. Everything else about the posting is public, and `citation_url` is that posting's own page on analyticslegends.ai. Quote it. Report `_meta.tranche_row_count` as the published public population, never as the size of the market.

find_sap_clients(limit, query, cursor, country, industry)

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 a subscriber API key, Legend tier or above. Verification status is SERVED, never silently filtered: `sap_client_verification_status` and `status` are columns on every row ('verified' on ~550 of ~21k rows), and you decide what standard of proof your answer needs.

get_concept(slug)

Fetch one concept entry by slug: title, category, level, tags and the editor's summary. Written by a named human editor, not generated. The card body, why-it-matters, key points and pro tip are subscriber content and are NOT returned — follow citation_url for those. Required: slug.

get_concept_card(slug)

The FULL encyclopaedia card for one concept — body, why-it-matters, key points, cheat sheet, glossary and pro tip, EN and FR — the corpus the €29.90 Consultant Pass sells. Requires a subscriber API key (Authorization: Bearer alk_…), Consultant tier or above; without one this tool refuses and get_concept keeps serving the public metadata. Find slugs with search_concepts. Required: slug.

get_day_rate_benchmark(country, seniority, specialisation)

The PUBLIC day-rate aggregate for SAP analytics freelance work: min/max daily rate by country, specialisation and seniority, each row carrying its own currency, source, source date and confidence. This is the free aggregate published at analyticslegends.ai/api/market-rates.json, and it is SMALL — 11 rows on 2026-08-09, every one of them a secondary source (a published market study or a job-board scan), sample_size null on 8 of them. NOTHING IS HELD BACK BEHIND IT: there is no paid counterpart to this aggregate — public.rate_contributions, v_community_rate_aggregates and v_rate_index are empty tables (measured 2026-08-09) — so whatever percentile a source row happens to carry is served here, free, to everyone. The GB row carries a median, p10 and p90, and its own note says its min/max ARE the 25th and 75th percentiles. What is missing from this answer is missing from THIS aggregate; it is not a paid tier. THIS IS NOT THE ONLY RATE THE PLATFORM PUBLISHES, AND ON THE QUESTIONS THIS MARKET ASKS MOST IT IS THE THINNER ONE. `find_opportunities` returns a `rate_band` on 1,421 of the 2,381 live radar postings (measured 2026-08-10) — a panel-inferred P25–P75 band per (seniority × product × region) cell, Eursap n=312 plus the Analytics Legends operator panel, and it is what each posting's public page leads with. It prices exactly the cells this 11-row aggregate cannot: Senior Datasphere DACH on 386 German postings, Senior BDC DACH on 52, where `specialisation:"bdc"` here returns nothing. When this tool comes back empty for a country × product, say the AGGREGATE holds no row and go read the radar band — do not report that the platform cannot price it. The two are different instruments: this one is a published market study, that one is an editorial benchmark attached to a live posting. Read `_meta.available_countries` / `available_specialisations` / `available_seniorities` — they are computed from the aggregate on every call — before concluding that a rate is unpublished, and quote each row with its own currency, its confidence and its source date.

get_firm(slug)

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 ~94 % of rows, meaning none declared here, never that the firm has no partners. Does not return the paid firm-intelligence profile, contacts, or any person. Required: slug.

get_firm_intel(name, limit, cursor, country)

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 ~39% of the corpus — glassdoor_rating, glassdoor_reviews_count and linkedin_followers are null on the entire corpus as of 2026-08-10, absence here is a data gap, not a signal). Requires a subscriber API key, Legend tier or above. Person-shaped fields (contacts, founders, leadership, recruiters, postal addresses) are NEVER served by this endpoint at any tier — they remain behind the platform's signed-URL path. Search by name; the public directory (search_firms) is a different, wider population.

get_sap_client_profile(id)

The full profile of one SAP end-customer — SAP footprint (products in use, modules known), analytics solutions, identity and evidence fields. Requires a subscriber API key, Legend tier or above. `id` comes verbatim from find_sap_clients.rows[].id. Required: id.

get_study(lang, slug, section)

Read one Analytics Legends study BODY — the paid text behind list_studies' metadata. Requires a subscriber API key, Consultant tier or above. 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. Required: slug.

list_firm_kinds

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.

list_sap_modules(limit, query, cursor)

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.

list_studies(lang, limit, query, cursor)

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 Consultant-tier deliverable, served by `get_study` on this same endpoint with a subscriber key. Use this to tell a reader that a study exists and where to read it.

search_concepts(limit, query, cursor, category)

Search the SAP analytics concept encyclopaedia — the vocabulary of the stack, written for practitioners. Returns titles and summaries; call get_concept for the full entry.

search_firms(kind, limit, query, cursor, country)

Search the published Analytics Legends directory of SAP analytics service providers — placement agencies, Big-4 and ESN practices, SAP vendors, platforms and community groups — by country, kind 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 kind label disagree in the SSOT it is the flag that decides what this tool serves, so read the flag's meaning into the answer and not the label's. PAGINATED: the whole matched set is reachable — pass `_meta.next_cursor` back as `cursor` with the same filters until it is null. When `query` is set, rows are ordered by how well the NAME matches it (exact, then prefix, then substring), and rows matching only the description come last; without `query` the order is the directory's own quality ranking.

search_news(limit, query, cursor, category)

Search the Analytics Legends market-news corpus. It is watched FOR SAP 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 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.

Last successful function declaration observed on 2026-08-13. Source: https://analyticslegends.ai/mcp. We list what the server declared; we do not call any of these functions.

Endpoint status observed on 2026-08-13. Source: https://analyticslegends.ai/mcp.

Signals

These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.

Where to get it

• Website — https://analyticslegends.ai
• Remote endpoint (streamable-http) — https://analyticslegends.ai/mcp