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Analytics Legends — SAP Analytics Intelligence

Search the public SAP analytics contract radar

find_opportunities
Read-onlyIdempotent

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 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, present on most 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.

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.
countryNoISO-3166-1 alpha-2 code, applied to both legs as a predicate on the row's own country_code. It NO LONGER selects the site-radar leg alone: the promoted feed carried country_code on almost none of its rows until 2026-08-28 and now carries it on most, so a country filter now returns both legs. A row still without one is dropped because it does not match, not because its leg was excluded by assumption. `_meta.match_count_by_leg` shows what each leg contributed on YOUR call — read the split there, never from a figure quoted in this text.
locationNoCity or place, matched case-insensitively as a substring of the posting's location. The promoted leg is all-German (Hamburg, Frankfurt am Main, Bremen, Munich, Cologne, Dortmund, Hanover, Landshut, Mannheim, Stuttgart); the site-radar leg is worldwide.
remote_modeNoRestrict to one work-location policy: `remote`, `hybrid` or `onsite`. READ THIS BEFORE ANSWERING A REMOTE QUESTION: a large share of the radar declares no policy at all (`_meta.remote_mode_undeclared` carries the live count — roughly half the radar when last measured, and a frozen pair written here drifted ~30% in two days), and an undeclared row is NOT an on-site row — it is a posting that does not say. Any value here therefore sets those rows aside rather than classifying them, exactly as the site's own filter does, and `_meta.remote_mode_undeclared` reports how many were set aside. The promoted leg carries its own `remote_mode` column and is filtered by the same predicate. Read `_meta.available_remote_modes` for the live spread before assuming a value exists.
employment_typeNoRestrict to one engagement type. THE RADAR IS MOSTLY PERMANENT, so a freelance or contract question answered off an unfiltered page is answered with salaried jobs. For the actual split, make the filtered call and read `_meta.tranche_total_row_count` — it is counted at query time. The promoted leg declared NO contract_type until 2026-08-28 and now declares one on most of its rows, so a value here no longer drops that leg wholesale — only the rows still silent. THOSE ROWS ARE NOT A FOURTH TYPE AND NOT PERMANENT ONES: `_meta.available_employment_types` counts only what declares, and `_meta.employment_type_undeclared` carries the rest, so the two together are the population and either alone is not. Read both before quoting a mix.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsYes
toolYes
_metaNo
_attributionYes
result_countYes

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior, and the description adds substantial behavioral context beyond that: the two public legs are near-disjoint, the promoted feed changed shape on 2026-08-28, most radar rows are permanent, rate_band is panel-inferred rather than employer-offered, and rows may be gated by member-only links. This is far more than the annotations alone provide.

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?

The description is long but front-loaded with scope and top-priority warnings. Most sentences carry real operational value for a complex and misleading dataset, though some instructions repeat, such as 'never from a figure quoted in this text' appearing multiple times. It is dense rather than wasteful, but not maximally lean.

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?

Given the tool's complexity, the rich output schema, and zero required parameters, the description is complete: it covers filter semantics, live `_meta` counters, rate-field differences, gated-link behavior, citation expectations, and per-leg shape changes. An agent can correctly select and invoke this tool from the description alone.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description adds crucial meaning beyond the schema: `query` does AND-style substring matching per word, `country` applies to both legs and silently drops rows without country_code, `remote_mode` sets aside undeclared rows rather than classifying them as onsite, and `employment_type` requires reading `_meta` counters to understand the true population split. This significantly exceeds the baseline for full schema coverage.

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?

The description opens with a specific verb and resource: 'Search every SAP contract and permanent-role posting Analytics Legends publishes to an ANONYMOUS visitor.' It clearly scopes the tool to the two public legs and explains what population it covers, which distinguishes it from sibling search/get tools without ambiguity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives strong contextual usage guidance, such as 'READ employment_type BEFORE CALLING THIS A CONTRACT MARKET' and instructions to read live `_meta` counters instead of quoted figures. It does not explicitly name sibling tools as alternatives or state when not to use this tool, so it falls just short of a 5.

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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TDQS

A4.5/5.0
Disambiguation4/5

Each tool targets a distinct resource or action (firms, clients, modules, concepts, studies, opportunities, rates, news, knowledge graph). Some pairs like find_academy_modules vs list_sap_modules and find_sap_clients vs search_firms could be confused, but the descriptions explicitly disambiguate them.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in lowercase snake_case: find_, get_, list_, search_, count_, query_. Verbs are consistently used for their roles (find/search for querying, get for single items, list for enumerations), with no mixed casing or style.

Tool Count4/5

20 tools is on the higher end, but the server covers a broad domain with multiple distinct datasets (directory, clients, academy, concepts, studies, opportunities, rates, news, graph). Each tool earns its place, though the count is slightly above the ideal 3-15 range.

Completeness5/5

The domain is a read-only intelligence platform, and it provides search/list and get operations for every major entity: firms, clients, modules, concepts, studies, and opportunities. The knowledge graph adds relational querying, and rates/news are covered. No essential lifecycle operations are missing for the stated purpose.