Insurers Global

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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds value by detailing what the returned text contains (regulator, class, verification level, register source, cite URL, dataset version/licence) and includes an important caveat about absence not implying absence of licence. This goes beyond the annotations' basic safety profile, though the description doesn't mention any pagination or error behavior (not needed here given the single-record nature and output schema).

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 information-dense and front-loaded with the action and resource. Each sentence contributes: id format, return contents, caveat, and usage guidance. No fluff, but it is slightly longer than necessary; still, every sentence earns its place.

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 is simple (one parameter) and has an output schema, so return details are covered. The description addresses usage context, alternatives, id format, and a key limitation. Nothing an agent needs to correctly invoke this tool 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?

Schema description coverage is 100% and the schema already describes the id format. The description reinforces and elaborates with concrete examples (e.g., 'insurer:micronesia-century-insurance-company-ltd') and explains the id must come from search. This adds practical clarity beyond the schema's bare description, so it earns a point above baseline.

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 explicitly states 'Fetch one record by an id returned from search' with concrete id format examples. It clearly differentiates from sibling tools like get_insurer, get_group, and get_country_summary by stating that for ordinary questions those should be preferred. The verb and resource are specific and 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 provides explicit usage context: 'Provided for ChatGPT deep research and company knowledge; for ordinary questions prefer get_insurer, get_group or get_country_summary.' It also specifies the required id format and gives an example, making it clear when and how to use this tool versus alternatives.

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=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds significant non-obvious behavioral context: counts count entities not licences, byClass sums to total, the regulator's own licence tally will not match, and large markets return a first page rather than the full list. These are exactly the quirks 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?

Five sentences, each earning its place: scope, count semantics, regulator mismatch warning, pagination behavior, and sibling routing. The core purpose is front-loaded in the first sentence, and there is no filler, repetition of schema content, or annotation duplication.

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?

Given the rich output schema (which covers return structure), the 100% parameter schema coverage, and strong annotations, the description covers everything an agent needs: what data comes back, how counts should be interpreted, how to page, and which sibling to use instead. No gaps remain for correct invocation.

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 100%, so the baseline is 3. The description adds value by explaining how offset interplays with the response (insurersPage.total and nextOffset to pass as offset), giving operational meaning beyond the schema's dry "Entity rows to skip" text. The compact and country parameters are already well documented in the schema, so the description need not repeat them.

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 opens with a specific verb+resource ("Get one country") and enumerates exactly what is returned: regulator details, currency, counts, verification status, market notes, and licensed insurers with register identifiers. It clearly differentiates from siblings by naming get_country_summary for facts-only use and implicitly distinguishing from get_insurer (full insurer records are per-slug there).

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 explicit routing guidance: "Prefer get_country_summary for market facts alone" directly tells the agent when this tool is NOT the right choice. It also explains the pagination usage pattern (insurersPage returns nextOffset to pass as offset) and when a single call suffices versus when paging is required, leaving nothing to inference.

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=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds crucial behavioral context: counts are per entity, not per licence; each company appears once; byClass sums to total; and regulator's licence tally is broader. It also notes it accepts a slug or ISO code. This goes beyond annotations without contradicting them, providing valuable interpretative guidance.

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 about five sentences, with the core purpose in the first sentence, followed by essential counting semantics, a warning, the parameter format, and an alternative tool. Every sentence adds value and is front-loaded, with no redundancy or fluff. It is efficient and well-structured.

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?

Given the tool has a single parameter, an output schema (indicated), and annotations covering safety, the description is complete. It explains what is returned, how counts work, when to use an alternative, and what input format is expected. An agent has everything needed to correctly invoke the tool and interpret results.

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 100%: the parameter 'country' is described in the schema as accepting a slug or ISO alpha-2 code with examples. The description repeats this but adds no new semantic meaning beyond what the schema already provides. Since the schema carries the full burden, a 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 states exactly what the tool returns: compact market facts for one country (regulator, currency, counts, verification, notes) and explicitly excludes insurer rows. This clearly differentiates it from sibling tools like list_insurers and get_country. The verb 'get' and specific resource 'country summary' make the purpose 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 explicitly says 'Use list_insurers with country for paged entity records,' directly telling the agent when to use an alternative. It also explains the counting semantics (entities vs licences) and warns that a regulator's own tally will not match, preventing misuse and misinterpretation. This is explicit when-to-use and when-not-to-use 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 read-only, idempotent, and non-destructive, so the safety profile is covered. The description adds that this is an edition-scoped metadata call and a precursor to querying, but it does not disclose additional behavioral details such as caching, freshness, or response size. No contradiction with 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?

Two sentences with no filler: the first front-loads the complete resource list, and the second provides the usage context. Every phrase earns its place.

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?

Given no parameters, an existing output schema, and read-only annotations, the description supplies the only missing context: what the tool returns and when to call it. The definition is complete for a simple directory/discovery tool.

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 zero parameters, so there is no parameter documentation burden for the description to carry. With 100% schema coverage and an empty schema, the baseline of 4 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 operation and resource: it gets dataset version, source dates, licence, coverage counts, endpoint URLs, filter vocabularies, and country metadata. It also differentiates from sibling retrieval tools by positioning itself as the slug-discovery entry point before querying.

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?

'Use this to discover supported country, class, ownership, region and continent slugs before querying' is an explicit when-to-use instruction. It does not name alternatives or exclusions, but among the siblings it is the clear directory/discovery tool, so the guidance is strong albeit not fully exhaustive.

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 behavior, and the description adds meaningful behavioral nuances: large groups return a matrix plus a first page, entitiesPage provides nextOffset, enrichment sources are named, and null enrichment means 'not collected' rather than 'no relationships'. This materially improves the agent's interpretation of results.

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 moderately long but every sentence delivers distinct value: definition, parameter constraints, paging, data sources, and null semantics. It is front-loaded with the most important concept 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?

Given the output schema exists, annotations are present, and the schema covers all parameters, the description fills the remaining gaps: response shape for large groups, paging mechanics, data provenance, and the meaning of empty enrichment. Nothing an agent needs to call the tool correctly 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?

Schema coverage is 100%, so the baseline is 3. The description goes beyond the schema by clarifying that group vs insurer changes the scope of the result, that paging applies to group footprints, and that the slug examples come from list_groups. This is useful but not exhaustive, so a 4 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 opens with a precise definition of 'licensed footprint' and ties it to a concrete matrix of legal entity, country, branches, passporting, and GLEIF parents. It clearly distinguishes group-level and insurer-level invocations, and the title's verb 'Get' matches the operation.

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 gives explicit usage context by saying to pass exactly one group slug from list_groups or an insurer slug, and explains the difference in what each returns. It does not explicitly name sibling tools as alternatives, but the usage conditions are clear enough for an agent to choose between group and insurer modes.

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=false. The description adds meaningful behavioral context beyond annotations: pagination behavior for large groups, the insurersDetailPage total, and the nextOffset usage for paging. This gives an agent a realistic picture of response size and iteration 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?

The description is two efficient sentences with zero filler. The primary purpose and return contents are front-loaded, and the pagination caveat is placed afterward without disrupting the core message.

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?

For a read-only lookup tool with an output schema, the description covers the resource identity, return content, pagination, and compaction behavior. Nothing an agent needs to decide whether to call this tool or to understand its high-level output 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?

Schema description coverage is 100%, and each parameter already has a clear description with defaults and constraints. The tool description adds some context around offset paging and compact rows, but it mostly restates or complements what the schema already explains, so the baseline 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 verb and resource ('Get one insurance group by slug') and enumerates exactly what it returns: home market, type, geographic coverage, licensed entity slugs, and their insurersDetail records. This clearly distinguishes it from list_groups and get_insurer.

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 for fetching a single group's aggregate data by slug, and the compact parameter explicitly points to get_insurer as the alternative for full insurer records. It does not explicitly name list_groups as the alternative for enumerating groups, so exclusions are only partially stated.

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=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds conditional availability language—'where available' and 'where one has been collected'—which warns that some fields may be absent depending on data completeness. This is useful 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 a single long sentence but is front-loaded with the core action ('Get one insurer by slug') and then packs the remaining space with a useful, non-redundant list of returned fields. It is efficient and has no filler, though it is slightly dense.

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?

An output schema exists, so return-value documentation is covered structurally. The description still conveys the breadth of the record (parent group, regulator, identifiers, profile data, etc.) and the conditional nature of some fields, which is sufficient for an agent to know what to expect.

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 100%, and the slug parameter is already fully documented with type, pattern, maxLength, minLength, and an example. The description adds no additional parameter-level meaning, so the baseline of 3 applies.

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 opens with a specific verb and resource—'Get one insurer by slug'—and then enumerates the exact fields returned. This clearly distinguishes it from sibling tools like list_insurers (plural listing) and search_insurers (fuzzy lookup) by narrowing scope to a single known identifier.

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 phrase 'by slug' gives clear context: use this tool when you already have a stable slug identifier. It does not explicitly name alternatives or state when-not-to-use, but the singular and identifier-based framing makes the intended use obvious.

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=true, idempotentHint=true, and destructiveHint=false, so no contradiction exists. The description adds minimal behavioral context by mentioning 'with their footprint', which hints at the returned shape, but it does not discuss pagination behavior, result ordering, or any operation-specific side effects.

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 one concise sentence, front-loaded with the core action and object, followed by a clear inclusion criterion and a note on output. It contains no filler or repetition.

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 is simple, read-only, and fully self-describing: the schema documents all four parameterschers, the annotations establish safety, and the output schema covers return values. The description provides the essential semantic filter, making the definition sufficient for reliable invocation.

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 input schema has 100% parameter description coverage, so the description does not need to repeat those details. It also provides no additional parameter-level semantics beyond what the schema already contains. This matches the baseline for well-documented schemas.

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 verb and resource: list insurance groups, and gives a concrete inclusion criterion (licensed entities in two or more countries). The phrase 'with their footprint' also clarifies the result contents; this differentiates it from single-entity tools like list_insurers and from the IAIS-specific list_iaigs.

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 inclusion criterion 'licensed entities in two or more countries' implies when the tool is appropriate, and the continent list suggests geographic scope. However, it does not explicitly state when to use list_groups instead of list_iaigs, list_insurers, or search_insurers, nor does it provide exclusion 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful context about the data source and the scope of results, but it does not disclose any additional behavioral traits such as pagination defaults, filtering semantics, or potential response volume.

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 front-loaded sentence that names the resource and its key output dimensions without filler. It is slightly dense and could be split for readability, but every clause adds relevant information.

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?

Given the output schema exists and annotations cover read-only/idempotent behavior, the description provides enough context about the data source, the resource type, and the included fields. Minor ambiguity around 'directory groups' could be clearer, but overall the tool is adequately described for invocation.

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 100%, so the input schema already documents limit, offset, in_directory, and jurisdiction. The description does not add extra parameter-level meaning beyond the schema, which places it at the baseline of 3.

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 ('List'), a specific resource ('IAIGs in the IAIS public register'), and the key data returned: group-wide supervisor, jurisdiction, and controlled directory groups, entities, and countries. This clearly distinguishes it from generic sibling tools like list_groups or list_insurers.

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 this tool is for retrieving IAIGs specifically from the IAIS public register, but it never explicitly says when to choose it over siblings such as list_groups or list_insurers. There are no when-not conditions or named alternatives, so the usage guidance is implied rather than explicit.

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=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds genuine behavioral insight beyond annotations: pages stop early for large records, nextOffset is reported, and country filtering returns full records while compact returns lean rows. This is valuable context not visible in the schema or 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 every sentence earns its place. It front-loads the core behavior in the first sentence, then packs filter guidance and response-shaping caveats into the following two sentences with no filler or repeated schema 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?

For a 9-parameter list tool with no required parameters and an output schema present, the description is complete: it covers all filter families, pagination behavior, response-size handling, and the compact-mode alternative. The output schema covers return structure, so the description does not need to restate it. Nothing an agent needs to call this tool correctly 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?

Schema description coverage is 100%, so the baseline is 3 flexibly, but the description adds meaning beyond the schema: it explains pagination defaults and bounds, gives concrete slug examples for class and group type, and clarifies the semantic difference between compact and full record returns. The parameter guidance is therefore above baseline without being redundant.

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 opens with a clear verb and resource: 'List licensed insurers with optional filters and bounded pages.' It immediately distinguishes the tool from single-record or search siblings by stating it lists insurers and supports filters. The pagination and compact-mode details further sharpen what this specific 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 clear context for when to use filters and compact mode, and references alternatives in the schema ('get_insurer returns the full record for any slug', 'list_groups returns them all'). It does not explicitly draw a when-not contrast with search_insurers, but the context is strong enough for an agent to route correctly.

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 goes further by disclosing paging behavior: it returns every match unless limit/offset is supplied, applies a default/cap, stops early for long results, and reports nextOffset. This is useful behavioral context 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 compact and front-loaded: the purpose is stated in the first clause, followed by concise paging behavior. Every sentence contributes meaningful information without 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?

Given the presence of a full output schema, complete parameter descriptions, and strong annotations, the description provides the remaining context an agent needs: what the resource is, what fields are returned, and how paging behaves. 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 all four parameters with 100% coverage, including defaults, maximums, and enum values. The description repeats the limit default/cap and adds the 'returns every match unless limit or offset is supplied' paging nuance, but that is largely additional framing rather than new parameter semantics.

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 and resource: 'List the insurance supervisory authority for every covered country,' and enumerates the included fields (name, acronym, website, currency, notes, register source). This clearly distinguishes it from sibling tools like list_insurers and list_groups, which target different entities.

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 clearly conveys when to use the tool: when you need insurance regulators across covered countries, optionally paged or filtered by region/continent. It does not explicitly name alternatives or exclusions, but the context is strong enough that an agent can select it appropriately against the sibling list.

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, and the description adds value beyond that: it discloses that results include what happened, when, and the current entity where one exists, and it explains the licence/policy continuation semantics. This is useful behavioral context without contradicting any annotation.

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 earn their place: the purpose, the trigger condition, the return shape, and the crucial failed-vs-successor nuance. No filler or repetition of schema details.

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 a full output schema, complete parameter documentation, and helpful annotations, the description covers the remaining context an agent needs: when to call it, what it returns, and how to interpret the outcome. 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?

Schema coverage is 100%, so the schema fully documents name, limit, and offset. The description reinforces that name is the former company name as printed on the document, but it does not add detail about paging or limits beyond what the schema already provides.

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 operation — resolving a defunct company name to the current insurer entity — and explicitly contrasts it with search_insurers, which covers names that do appear. This distinguishes the tool from siblings 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.

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

It states the precise trigger: use when an older document names an insurer not found in search_insurers due to renaming, merger, or delisting. It also names the alternative (search_insurers) and clarifies that merged/acquired insurers retain their licence, with only 'failed' meaning the company failed.

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 cover safety (readOnlyHint, idempotentHint, destructiveHint), so the description's job is lighter. It adds meaningful behavior: returns up to 20 results, provides an id for the fetch tool, and includes a citable insurers.global URL. This goes beyond the annotations without contradicting them.

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?

Two compact sentences deliver scope, result cap, output contents, use case, and a routing instruction. Every clause adds value, and the most selection-relevant information appears first.

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?

For a single-parameter read search with a full output schema and strong annotations, the description covers the searchable universe, result limit, output purpose, and the sibling alternative. There are no obvious gaps that would prevent an agent from calling it 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 input schema already fully documents the single parameter ('Company, group or country name, or part of one.') at 100% coverageable. The description reinforces the accepted entity types, including former company names, but does not add substantial new meaning beyond what the schema already provides.

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 ('Search'), the exact resource types (licensed insurers, insurance groups, countries, former company names), and geographic scope. It also explicitly differentiates this tool from search_insurers, so an agent can identify which tool to use without opening either schema.

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 says this tool is 'Provided for ChatGPT deep research and company knowledge' and explicitly tells agents to 'prefer search_insurers' for ordinary questions. This gives clear selection criteria and names the alternative, leaving no ambiguity about when to invoke this tool.

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 establish that this is read-only, idempotent, and non-destructive, so the description does not need to restate safety. It adds modest context by noting that results include slugs and links, but it does not disclose matching semantics, case sensitivity, or pagination behavior. This is acceptable given the existing 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?

Two sentences, no filler. The first sentence front-loads the search action, entity types, and geographic coverage; the second states the return payload. Every sentence earns its place.

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?

For a low-complexity search tool with a full input schema, output schema, and rich annotations, the description covers the essential details: what is searched, where, and what comes back. It could mention result ordering or pagination, but those are minor gaps given the structured metadata already present.

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 100%, with all three parameters (query, limit, offset) already explained. The description's 'by name' phrase adds nothing beyond the query parameter's schema description, so the baseline of 3 applies.

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 verb ('Search'), the resource (licensed insurers, reinsurers, takaful operators, groups) and the geographic scope, and states what is returned. It does not explicitly contrast itself with sibling list/get tools, but the 'by name' matching language makes the core purpose 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 use case is implied: use this when you want to find insurers, countries, or groups by name. However, there is no explicit guidance about when to prefer this over sibling tools like list_insurers, list_groups, or get_insurer, and no when-not-to-use conditions.

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