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Analytics Legends — SAP Analytics Intelligence

by ai.analyticslegends

Count the firm directory by country, kind, module or SAP signal

count_firms_by
Read-onlyIdempotent

Answer a COUNTING question about the published firm directory in one call: how many organisations per country, 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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
byYesWhich facet to count on. Required — there is no default worth guessing.
kindNoRestrict to one organisation kind before counting, same vocabulary as search_firms. Combining it with `by:"kind"` is legal and returns that single bucket.
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.
moduleNoRestrict to organisations with a DECLARED link to one SAP module code before counting, same vocabulary as search_firms' `module`. Combining it with `by:"module"` is legal and returns that single bucket (plus the null bucket).
countryNoISO-3166-1 alpha-2 country code, e.g. DE, FR, CH.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsYes
toolYes
_metaNo
_attributionYes
result_countYes

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already provide readOnlyHint, idempotentHint, and destructiveHint, lowering the burden. The description adds valuable behavior beyond those hints: null buckets are real and served rather than hidden, counts reflect the full population, and filters match search_firms exactly. This prevents an agent from misinterpreting missing facets.

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

Conciseness5/5

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

Three sentences, each earning its place: purpose, usage rationale, and a crucial data-completeness behavior. It is front-loaded with the core action and keeps the details relevant rather than padding.

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?

For a read-only counting tool with a rich input schema and an output schema present, the description covers the essential context: what it counts, how it relates to search_firms, when to prefer it, and how null buckets behave. Nothing critical is missing.

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 schema already documents each parameter well. The description still adds useful relational context: the filters are the same ones search_firms accepts, and counting a filtered slice is as easy as counting the whole. This helps an agent reuse knowledge of a sibling tool.

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: 'Answer a COUNTING question about the published firm directory'. It names the four facets (country, kind, module, SAP signal band) and explicitly contrasts with search_firms, so an agent can tell this tool from its sibling without opening schemas.

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?

It gives explicit when-to-use guidance: use this instead of paging through search_firms and tallying rows. It even quantifies why, citing hundreds of calls and megabytes of rows, which makes the trade-off concrete and actionable.

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.

Resources