Analytics Legends — S… vs Zambo — MCP Server Comparison | AllMCPs
Analytics Legends — SAP Analytics Intelligence vs Zambo
A side-by-side look at the Analytics Legends — SAP Analytics Intelligence and Zambo MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Analytics Legends — SAP Analytics Intelligence for developer tools work over a hosted endpoint; choose Zambo for developer tools over a hosted endpoint. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Analytics Legends — SAP Analytics Intelligence when
You need dedicated capabilities in the Developer Tools domain.
You prefer a remote HTTP/SSE transport.
You want tools like search_firms, count_firms_by, get_firm.
Analytics Legends — SAP Analytics Intelligence tools 22
search_firmsSearch 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 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.
count_firms_byAnswer 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 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.
get_firmFetch 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-intelligence profile, contacts, or any person.
list_firm_kindsBreakdown 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_freelance_platformsThe 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 proven to refuse signups — it is unassessed or unpublished.
find_opportunitiesSearch 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, BTP, ABAP), all German cities, dated (posted_at is populated on EVERY active row of that leg — an invariant held since 2026-07-31, not a snapshot). 🔴 THIS LEG CHANGED SHAPE ON 2026-08-28: until then it was fed by three keyless APIs and carried no contract_type, country_code, expires_at or rate at all; it was then loaded from the site radar and now declares contract_type and country_code on most of its rows, an expiry on most, and an advertised rate on a small minority. Do NOT assume a field is null on this leg — read the `_meta` counters on YOUR OWN response, which are computed at query time; (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, so an unfiltered page answers a freelance question with salaried jobs unless you filter. The argument of the same name does the filtering, and `_meta.tranche_total_row_count` on your own response is the live population — read the split from a filtered call, never from a figure quoted in this text. 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 — most listings publish no rate at all. `rate_band` is the platform's editorial benchmark for that posting's (seniority × product × region) cell. It is a SUBSCRIBER surface of the WEBSITE, not of this server: the radar file this server reads carries no band since 2026-08-29, so `rate_band` is null on EVERY row here, with or without a key (each response's `_meta.rate_band_coverage` counts it). The panel grid itself covers European cells only — DACH, FR/Benelux, UK/IE, Southern EU, Eastern EU, Nordics — so a posting in the Americas, APAC, Africa or the Middle East has no band at ANY tier: never promise one for it. 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.
search_newsSearch 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.
search_conceptsSearch 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 Consultant-tier: call `get_concept_card`. Why-it-matters and key points are NOT served by this endpoint either, but they are published in full on the concept page at citation_url — follow the URL for those, a plan buys the body, not them.
get_conceptFetch 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 full on the concept page at citation_url — follow the URL for those.
list_studiesList 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. 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; each row carries its `editions` list. Pass `lang` to get one row per study.
get_day_rate_benchmarkThe 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_size` is null on most of them. NO COUNT IS WRITTEN HERE ON PURPOSE: `_meta.tranche_total_row_count` and the rows themselves are the live measure. A frozen pair stood here until 2026-08-27 — '11 rows on 2026-08-09 … sample_size null on 8 of them' — and the second half was WRONG (7 of 11) while the first was still right, which is the whole argument against writing either. NOTHING IS HELD BACK BEHIND IT: there is no paid counterpart to this aggregate. The community-contribution path exists (public.rate_contributions) but publishes nothing yet — v_community_rate_aggregates and v_rate_index are still empty, because a contributed rate only surfaces once a cell holds enough submissions to be reported without identifying anyone. 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. The website carries a `rate_band` per live radar posting for signed-in subscribers — a panel-inferred P25–P75 band per (seniority × product × region) cell, Eursap n=312 plus the Analytics Legends operator panel. This server does not serve it (`find_opportunities.rate_band` is null on every row, with or without a key). The grid covers EUROPEAN cells only — DACH, FR/Benelux, UK/IE, Southern EU, Eastern EU, Nordics — and there it prices exactly the cells this small aggregate cannot (Senior Datasphere DACH, Senior BDC DACH), where `specialisation:"bdc"` here returns nothing. When this tool comes back empty for a country × product: in one of those European regions, say the AGGREGATE holds no row and that subscribers read the platform's band on the posting pages; ANYWHERE ELSE (Americas, APAC, Africa, Middle East) say the platform publishes no rate for that cell at any tier — never that subscribers get one. 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.
list_sap_modulesThe 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.
workflow_closeCreate or return an immutable snapshot of this job's eligible receipts. Repeating the call with unchanged receipt data returns the same snapshot workflow_id; later calls after new receipts create a separate snapshot while session_workflow_id continues identifying the live ledger. Both identifiers are unbound metadata excluded from canonical receipt bytes. The first close returns a one-time private workflow_manage_token; retries do not rotate or reissue it. The workflow_close summary receipt is excluded from the step count. Records Zambo's claims, not issuer authentication.
workflow_sealSeals a closed workflow for durable export. Requires the matching job session and an active pass. Never spends, captures, or starts a payment. The export remains a Zambo claim, not issuer authentication.
zambo_universalUniversal Zambo entry point for routing natural-language requests across supported Zambo tools through one MCP connection. Covers strategy, code audits, lead generation, wallet intelligence, provenance certificates, swarm coordination, and live market data. Returns a route, execution state, downstream tool results when available, and receipt information.
new_sessionStarts fresh session context and returns a new session_id. Reuse that ID on subsequent calls for continuity; changing or omitting it starts clean context.
continuity_get_recentReads only the private receipt timeline for the supplied job or thread ID. Returns receipt links, has_more, and a cursor for the next page. Use since to filter by an inclusive RFC3339 timestamp. Optional AI client labels are self-reported, not verified.
continuity_list_threadsLists named threads owned by the supplied job ID. Thread IDs can be used as _session_id by another AI client.
continuity_open_threadOpens a named thread or thread ID and returns only events with stored Zambo receipts, including links to open and check each receipt.
continuity_start_threadCreates or reuses a named thread under an existing job ID. Pass the returned thread_id as _session_id to append work from another AI client.
continuity_set_prefs
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Analytics Legends — SAP Analytics Intelligence is categorized under Developer Tools and uses a remote HTTP/SSE transport. Zambo belongs to Developer Tools and uses a remote HTTP/SSE transport. Pick Analytics Legends — SAP Analytics Intelligence for developer tools work and Zambo for developer tools.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Analytics Legends — SAP Analytics Intelligence has no declared pricing — check the project's own site. Zambo is listed as free. Auth: no auth required. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add analytics-legends-sap-analytics-intelligence-2 and zambo as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Analytics Legends — SAP Analytics Intelligence has a quality signal of 55/100 (Good) with 0 views, 0 installs and 0 GitHub stars. Zambo has 78/100 (Great) with 9 views, 0 installs and 0 GitHub stars.
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