ranked

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

Annotations already mark it read-only, idempotent, and non-destructive. The description adds useful transparency about snapshot-date access, the regulator's own ordering, returned URLs, and explicitly clarifies that this is not a Ranked licence finding.

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

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

The description is structured and mostly efficient, but the final disclaimer about not being a licence finding is repeated with similar wording earlier. Still, it remains readable and front-loads the core action and scope.

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

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

With no output schema, the description helpfully states that each row includes the register's statement, hub_url, feed_url, and place_url where applicable. It also defines what a register row is. Minor gaps remain around response shape and pagination details, but the core context is covered.

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

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

Only the 'category' parameter is described in the schema; 'regulator' has an enum but no explanatory text, and 'limit'/'offset' are left implicit. With 25% schema coverage, the description partially compensates by explaining the category filter but leaves standard pagination and regulator semantics underspecified.

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

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

Clearly states the verb 'List' and the resource 'entries on a Nigerian regulator's register', with the regulator and optional licence-class filter. It also distinguishes itself from a ranking by noting the register's own order, separating it from ranking tools.

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

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

Explains optional filtering and the source/order semantics, but does not explicitly say when to prefer this tool over siblings such as ranked_check_licence or ranked_rankings. The guidance is implicit rather than a direct comparison.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds significant behavioral context: 'unknown' is an honest answer rather than a data gap, and it explains the Nigerian card limitations that make 'accepts Visa' misleading. This is exactly the kind of nuance an agent needs to interpret results correctly.

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

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

The description opens with the primary use case, immediately lists the returned dimensions, and closes with a short, valuable caveat about 'unknown' and Nigerian card limitations. Every sentence carries useful information, and the structure front-loads the most important semantic content.

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

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

Despite lacking an output schema, the description enumerates all major return dimensions: naira pricing, card compatibility, local rails, local entity, geo-restriction, workaround, and pages_to_verify. It also defines the meaning of 'unknown' so an agent can correctly interpret ambiguous results. The single required parameter is fully described by the schema, so nothing essential is missing.

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

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

The schema already documents the single 'name' parameter as 'product or service name, or its domain,' so schema coverage is 100%. The description restates 'named product or service' but adds no new format, examples, or syntax details. With full schema coverage, the baseline score of 3 is appropriate.

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

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

The description names a specific question ('can someone in Nigeria actually pay') and a specific resource ('a named product or service'), then enumerates the concrete dimensions it returns. This gives the tool a clear identity that distinguishes it from sibling tools like ranked_check_licence or ranked_compare. It is not a tautology and leaves no doubt about what the tool does.

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

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

The description gives a clear trigger condition: use it when you need to know whether a Nigerian can pay for a specific product or service, including naira pricing and local rails. It does not explicitly name alternatives or say when not to use it, but the scope is obvious enough that an agent can decide without opening sibling schemas.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

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.

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.

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.

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.

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.

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

The annotations already declare readOnly, idempotent, and non-destructive behavior, and the description adds substantial behavioral context beyond them: input order is preserved, Ranked-operated products are partitioned into a separate unranked block, are flagged with operated_by_ranked=true, and are never ordered above others. The note that evidence tier indicates provenance rather than quality is also meaningful and non-obvious.

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

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

The description is compact and information-dense. Every sentence earns its place: core operation, count constraint, comparison dimensions, ordering behavior, operated-by-Ranked handling, and evidence-tier semantics. There is no filler or redundancy.

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

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

The tool has a single parameter and no output schema, but the description covers the essential invocation semantics and the important behavioral quirks, including the special handling of Ranked-operated products. The annotations cover safety and idempotency, so the overall picture is complete enough for an agent to call this tool correctly.

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

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

The schema has no parameter descriptions and 0% coverage, so the description is the sole source of semantic meaning. It explains that the names parameter accepts 2-5 named products or services, specifies the comparison dimensions, and clarifies that the order of the names array controls output row order. This fully compensates for the bare schema.

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

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

The description uses a specific verb ('Compare') and a specific resource ('products or services on Nigeria-usability'), and it names the four concrete criteria (naira pricing, card acceptance, local entity, geo-restriction). It also differentiates itself from ranking tools by explicitly stating that rows are returned in the given order and not ranked.

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

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

The description gives clear context: use this tool when you need to compare 2-5 named products or services on Nigeria-usability. It does not explicitly name sibling tools as alternatives, and it lacks an explicit when-not-to-use statement, but the trigger conditions are clear and no alternatives are wrongly implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already declare readOnly/idempotent/non-destructive; the description adds meaningful behavior beyond that: operated_by_ranked products are returned in a separate unranked block, never ordered above others, and evidence tier is provenance not quality. It also documents the common result fields, making the tool's behavior concrete without contradicting annotations.

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

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

Four dense sentences, each contributing unique information: dataset scope and size, result fields, sibling routing, ordering rule, and evidence-tier caveat. It is front-loaded and contains no filler.

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

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

With no output schema, the description carries the full burden of explaining returns, and it does: every result carries url/source/source_url/accessed_on, register-backed variants add register entry and access date, and ranking behavior for Ranked-operated products is stated. Combined with the annotations and sibling routing, an agent has enough to call and interpret this tool correctly.

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

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

The schema already documents query as name/domain/category/licence type and limit has default/min/max constraints, so the description does not need to restate them. It adds output-structure context (result fields, unranked block) but does not further explain how query or limit behave, leaving limit semantics mostly to inference.

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

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

Description opens with a specific action ('Search Ranked's dataset...') and defines the resource scope ('Nigerian-relevant businesses, institutions, websites and apps'), then distinguishes itself from live-rank/place siblings. The sibling reference and dataset composition make it easy to tell apart from ranked_rankings and ranked_place.

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

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

It explicitly routes live-ranking and place-page needs to ranked_rankings / ranked_place, and the 'file-backed dataset' framing establishes when this tool is appropriate. It does not enumerate all sibling distinctions (e.g., ranked_by_regulator), but the main alternative is clearly identified.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the description only needs to add behavioral context. It does so by disclosing kind-dependent output semantics, counts, state filtering thresholds, and that data hubs are file-backed with no database needed. This adds meaningful value beyond the annotations.

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

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

The description is dense but front-loaded: it states the core purpose first, then maps each kind to its meaning. It is a single long sentence with semicolon-separated clauses, which packs information efficiently but could be more readable as a structured list. No filler words are present.

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

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

The description covers all required parameter values, URL formats, and filtering rules, which is enough for an agent to invoke the tool correctly. It does not specify the exact return format or the effect of the optional 'limit' parameter, but the described 'list pages with counts' gives a reasonable expectation given no output schema.

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

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

Schema description coverage is 0%, so the description must compensate. The 'kind' parameter is fully explained with concrete URL patterns and conditions for every enum value. The 'limit' parameter is not described, but its schema already provides default, minimum, and maximum, making it reasonably self-explanatory.

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

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

The description clearly identifies the tool as providing citable list pages (hub URLs) on ranked.ng, with counts. It enumerates the four kinds (category, city, state, data) and shows URL patterns, making the resource and scope specific. It does not explicitly contrast with siblings, but the resource and use-case are unambiguous.

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

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

The description states the intended use case: list pages an assistant can cite, with counts. It gives clear context for when to use the tool and what each kind yields, including conditions such as states with at least 25 operational listings. It does not name alternatives or exclusions, but the context is sufficiently clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Beyond readOnly/idempotent annotations, the description reveals meaningful behavior: business phones are public, verified tier is evidence-only, ranks apply only to the primary PUBLISHED facet, register matches carry 'as accessed on' provenance, and non-OPERATIONAL places are excluded unless includeClosed=true. This gives the agent a realistic model of what the data means and its freshness semantics.

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

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

Three dense sentences with the core purpose front-loaded before field enumeration. The field list is long but necessary because there is no output schema; however, a single monolithic sentence makes it slightly harder to scan than structured bullets would.

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

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

With no output schema, the description carries the full burden of explaining return values, and it does so thoroughly: identity, category, location, contact, ratings, verification, rank, regulator provenance, data freshness, and observation history. Parameter semantics are covered, and the closed-place exclusion closes the main behavioral gap.

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

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

Schema already describes city, name, and slug; the description adds the URL-tail interpretation of slug, the slug-or-name+city combination rule, and explains includeClosed's effect (include non-operational places), which is not documented in the schema. Since coverage is 75%, this extra semantic filler is valuable.

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

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

Description begins 'One business listing from the production database' and immediately specifies lookup keys (slug or name + city), making the resource and action unambiguous. It also summarizes the specific returned payload (rank, register matches, observations), clearly distinguishing it from sibling search/ranking tools.

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

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

Clear usage context: fetch a single place by slug URL tail or by name+city, with includeClosed controlling closed-place visibility. It doesn't explicitly name sibling alternatives or state when to prefer this over ranked_find_business, but the single-page scope and lookup keys provide strong situational guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already mark the tool as read-only, idempotent, and non-destructive; the description adds substantial behavior beyond that: 'top 25 at most,' slug/name acceptance, a conditional published/unpublished failure shape, the exact entry fields, provenance details, methodology link, and explicit statements that paid placement never affects rank and Ranked products are never ranked.

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

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

The description is front-loaded with the core purpose and packed with operationally relevant details, including response structure, constraints, and integrity guarantees. It is somewhat long and includes rhetorical flourishes like 'the one fact nobody else has,' but for a tool with no output schema, the density is mostly justified.

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

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

With no output schema, the description carries the full burden of explaining response shape, and it does so thoroughly: per-entry fields, per-answer fields, failure behavior, URL pattern, data provenance, and methodology link. It leaves little ambiguity about what an agent will receive or what constraints apply.

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

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

Schema descriptions already cover category and city with examples; the description reinforces slug-or-name acceptance and adds that results come 'straight from the production database' and are capped at 25, which clarifies limit's practical effect. It does not add significant meaning to the limit parameter beyond the schema's min/max/default, but it complements the schema well.

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

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

The description names a specific resource — Ranked's ordered list for a category in a Nigerian city — with a clear verb ('ranking'), and the category-plus-city scoping makes the tool's job unambiguous. It does not explicitly contrast itself with siblings like ranked_place or ranked_stats, so it stops short of full sibling differentiation.

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

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

The description implies usage when a category-and-city ranking is needed and adds the important precondition that it is served only when the base page is published. It does not explicitly state when to prefer this tool over siblings, nor does it name alternatives or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

The description mentions 'file-backed dataset' and 'whether the live database is configured,' giving some insight into internal checks. It does not contradict the readOnly/idempotent annotations, and it adds context beyond them. However, it does not elaborate on potential edge cases or side effects (which are minimal for a read-only tool).

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

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

The description is a single long sentence with a dash and semicolon, creating a run-on structure that is difficult to parse. Phrases like 'file-backed dataset by evidence tier, kind and regulator with each register's access date' are cluttered and reduce clarity. It would be more concise and structured if broken into separate sentences or bullet points.

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

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

The description provides fragmented details (counts, configuration status, provenance rules) but does not clearly specify the output format or what exactly the tool returns. It lacks a straightforward explanation of the return value, making it incomplete for an agent to confidently use the tool without further inference.

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

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

The tool has no parameters, so schema coverage is effectively 100%. Per the guidelines, this yields a baseline score of 3. No additional parameter details are necessary.

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

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

The description suggests a summary/statistics tool with 'Counts of what this server holds' and explicitly states 'Use to explain provenance to a user.' However, the verb is missing; it is unclear whether the tool returns counts, displays them, or performs something else. The phrasing is more of a content description than a clear action.

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

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

The description gives a direct usage scenario: 'Use to explain provenance to a user.' This provides clear guidance on when to invoke this tool. It does not contrast with sibling tools, but the stated purpose is specific enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.