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ranked

Does this name appear on a Nigerian regulator's register?

ranked_check_licence
Read-onlyIdempotent

Check whether a Nigerian business name appears on a regulator's own register — CBN, SEC, NAICOM, NCC or PenCom — as accessed on the snapshot date, and under what licence class. Exact normalised-name match first, then partial. Each match carries the register's statement ("Appears on the register as accessed on ..."), the hub_url, the JSON feed_url, and place_url when Ranked has a matching indexable listing. A register row is what the regulator's register listed on the access date. It is not a licence finding by Ranked, registers lag revocations, and absence from Ranked's copy is not proof that a name is unlicensed. Ranked never says "licensed" in its own voice.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesbusiness name as on the offer, policy or app
limitNo
regulatorNoany

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.7/5.0
Behavior5/5

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

Beyond the annotations, the description openly discloses that registers lag revocations, absence from Ranked's copy is not proof of unlicensed status, Ranked never says 'licensed' in its own voice, and that results reflect the snapshot date. It also reveals match ordering and the exact payload fields, which is exemplary transparency.

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?

The description is dense but every sentence earns its place: purpose, match behavior, result contents, and critical limitations. It is front-loaded with the main purpose and uses later sentences for necessary caveats without repetition or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no nested objects, the description does a strong job of explaining what each match carries (statement, hub_url, feed_url, place_url) and what a register row means. It falls slightly short by not describing no-match behavior, how the limit parameter shapes results, or how the 'any' regulator option behaves.

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

Parameters3/5

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

Schema coverage is only 33%, so the description must compensate. It adds useful context for the name parameter (normalised exact match first, then partial) and indirectly for regulator by listing CBN, SEC, NAICOM, NCC and PenCom. However, the limit parameter is never explained, and its effect on result pagination or count is left entirely to inference.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Check whether... appears'), a concrete resource (a Nigerian business name against regulator registers), and explicitly names the five regulators and the licence-class outcome. It is very clear, but it does not explicitly distinguish itself from siblings such as ranked_by_regulator, so it stops short of a 5.

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

Usage Guidelines2/5

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

The description explains what the tool does and its caveats, but it never says when to use this tool instead of a sibling, when not to use it, or what prerequisites apply. No alternative tool is mentioned, so an agent gets no routing guidance.

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

A3.9/5.0
Disambiguation5/5

Each tool targets a distinct query type: bulk regulator lists, single-name licence checks, single-product payment eligibility, multi-product comparison, dataset search, place details, rankings, hub browsing, and stats. Descriptions explicitly cross-reference the related tools (e.g., find_business directs users to rankings/place), so an agent should not confuse them.

Naming Consistency4/5

All tool names share the consistent ranked_ prefix and snake_case style, which is predictable. However, the suffix mixes clean verb-object forms (check_licence, find_business, compare) with other patterns (by_regulator, can_nigerian_pay, place, rankings, hubs), so it is not a uniform verb_noun convention.

Tool Count5/5

Nine tools is well within the ideal range for a read-only Nigerian business/regulator data server. Each tool earns its place by covering a distinct query workflow: register lookups, licence checks, payment eligibility, comparison, search, place details, rankings, hub navigation, and dataset stats.

Completeness4/5

The surface covers the core domain thoroughly: bulk regulator access, name-based licence checking, product usability, place details, rankings, search, and provenance stats. Minor gaps remain, such as no explicit state-level ranking endpoint and limited ability to list every place in a category beyond the top-25 published ranking, but there are no obvious dead ends.

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