Tizona — Australian business identity tools

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

With no annotations, the description carries the transparency burden. It discloses important behavioral details: calculations are done in integer cents to avoid rounding drift, and it warns about the one-eleventh rule for inclusive amounts. This adds meaningful context beyond the 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 compact and front-loaded. The first sentence states the core operations, the second explains the rounding behavior, and the third highlights a common pitfall. Every sentence earns its place with 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?

For a simple two-parameter calculation tool, the description is largely complete. It covers both modes, rounding behavior, and a common error. It does not describe the return format, but the output of a GST calculation is reasonably predictable and no output schema exists to fill that 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 coverage is 100%, so the baseline is 3. The description adds value by explaining how the two parameters interact: amount is the money value and basis determines whether GST is added or extracted. The one-eleventh warning clarifies the inclusive basis semantics beyond the schema's simple 'Whether the amount already includes GST.'

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 states the tool's function with specific verbs: 'Add GST to a GST-exclusive amount, or extract the GST contained in a GST-inclusive amount.' This precisely distinguishes the two calculation modes and leaves no ambiguity 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 explicit guidance on when to use each mode: use 'exclusive' when adding GST, use 'inclusive' when extracting GST. It does not name alternative tools, but the usage context is clear enough for an agent to select the correct basis.

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?

With no annotations provided, the description fully carries the behavioral burden. It discloses the output range, legal-form normalization, word-order insensitivity, generic-word down-weighting, obfuscation handling with an 'obfuscation' field, and the 'distinctiveness' caveat. This is rich, non-obvious behavioral detail that helps the agent set expectations.

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 opening sentence front-loads the core purpose, and each paragraph adds meaningful behavioral detail. It is longer than strictly necessary, but the extra content covers important edge cases and user-visible output fields, so it 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 has no output schema, so the description reasonably covers key result aspects: score range, reasoning, obfuscation indicator, and distinctiveness figure. It could be more complete by detailing the exact output structure or how to interpret the distinctiveness value, but for a comparison tool this is a solid, usable level of detail.

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%: 'a' and 'b' are described as names, and 'type' states it defaults to company. The description adds context about legal forms and matching behavior but does not deepen parameter-level semantics beyond the schema, so the baseline 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 clearly states a specific action: 'Score whether two names refer to the same entity, from 0 to 1,' and names the exact resource (two company or person names). It is distinct in function from siblings like normalise_entity_name, but it does not explicitly differentiate itself from match_entity_name or deduplicate_entity_names.

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 but never says when to choose it over sibling tools such as deduplicate_entity_names or match_entity_name. There are no explicit 'when to use' or 'when not to use' statements, leaving the agent to infer guidance from the sibling names.

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?

No annotations are present, so the description carries the full transparency burden. It discloses the non-exhaustive grouping heuristic ('grouped by distinctive words'), the number of comparisons reported, and the `truncated` flag as an incomplete-result signal. This goes beyond a basic functional statement, though it does not explicitly mention side effects or permissions (e.g., read-only), which would be expected in a fully transparent description.

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 concise, two short paragraphs. It front-loads the core action in the first sentence, then adds relevant behavioral details and a practical failure-mode warning. Every sentence contributes useful information without redundancy 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, the description partially fills the gap by mentioning the comparison count and the `truncated` flag. However, it omits details about the output format (e.g., cluster structure, how entities are represented) and does not explain `type` or `threshold` adequately. Given the tool's moderate complexity, the description is usable but missing key pieces for full understanding.

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 low (only 33%: `threshold` has a note about default). The tool description does not explain the `type` parameter or clarify the meaning of `threshold` (e.g., similarity cutoff). It only implicitly refers to names and the algorithm. With such low coverage, the description fails to compensate, leaving parameters under-specified beyond the 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 clearly states the tool's function: 'Group up to 250 names into clusters that appear to be the same entity,' and provides real-world context (CRM merge, supplier list cleanup). It distinguishes this tool from siblings like `compare_entity_names` and `match_entity_name` by focusing on batch clustering rather than pairwise comparison or individual matching.

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 contextual guidance (e.g., for CRM merge or cleanup) and explicit advice on handling the 'truncated' case (split the list and retry). It does not name alternative tools or state when not to use it, but the purpose is clear enough to infer appropriate use alongside sibling tools.

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?

No annotations are provided, so the description carries the full burden. It discloses behavior: returns only those above threshold, ranks them, and includes position in the input array for correlation. It also mentions the 750-candidate limit. It does not explicitly state read-only nature, but the match operation implies no mutation; this is acceptable for a scoring 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?

Two dense sentences, each earning its place. The first fronts the core action and output format; the second gives usage context. No redundancy or 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?

No output schema exists, but the description adequately explains the return shape (ranked, above threshold, with position). It also covers constraints (750 candidates, threshold default) and usage. Missing details like error handling or score meaning are not critical for this complexity level.

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 has descriptions for 4 of 5 parameters (80% coverage), including query, candidates, limit, and threshold; type is self-documenting via enum. The description adds little beyond the schema—mentioning 750 candidates repeats the schema's maxItems, and the output position is about results, not parameters. With high schema coverage, a 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?

Description states the verb 'Score' and resource 'one name against up to 750 candidates', and specifies the output (threshold, ranked, with position). It clearly distinguishes from siblings like compare_entity_names by noting the use case is finding a customer in an existing list rather than comparing pairs one at a time.

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?

Explicitly says 'Use this to find a customer in an existing list rather than comparing pairs one at a time.' This provides clear context and contrast with an alternative approach, giving an agent a strong signal on when to use 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 are absent, so the description carries the full burden. It discloses that the tool reports every token removed ('nothing disappears without explanation') and flags any obfuscation, providing transparency about its output behavior. However, it does not mention error handling, return format, or side effects (e.g., potential data loss), though the transparency about removed tokens is a strong positive.

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 is concise and information-dense. Every clause adds value: purpose, use case, and key behavioral guarantees. No wasted words.

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's moderate complexity (2 params, no output schema), the description covers the main purpose and key behavioral traits (token reporting, obfuscation flagging). It lacks explicit details on input requirements (e.g., expected format) or the output structure, but the absence of an output schema and the simple schema make this adequate. The description is informative enough for an agent to decide when to use it.

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 50% (only 'name' has a description; 'type' is an enum with no description). The description adds meaning by explaining the purpose of normalisation (separating legal form) and mentions token removal, which helps understand the 'name' parameter. It does not explain the 'type' parameter, but the enum values ('company', 'person') are somewhat self-explanatory, though a brief mention of its role would be better.

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 states the tool's purpose: 'Reduce a name to its comparable core with the legal form separated out' — a specific verb (reduce) and resource (name) with a clear outcome. It does not explicitly distinguish itself from siblings like compare_entity_names or match_entity_name, but the focus on normalisation (canonical form) is implied.

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 as 'a stable grouping key', which gives a concrete use case but does not explicitly contrast with alternatives (e.g., when to use compare_entity_names vs this). No explicit exclusions are provided, but the grouping key context hints at when it is appropriate.

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?

With no annotations provided, the description carries the full burden. It discloses the output fields (source, publication date, hash, size, age) and adds context about the freshness field, implying a read-only operation. It doesn't mention error conditions or permission requirements, but for a simple metadata query this is adequate and not misleading.

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 sentences, each earning its place: the first states the action and content, the second explains usage and a key caveat. It is front-loaded and free of 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?

For a zero-parameter tool with no output schema, the description fully covers purpose, usage context, and expected output fields. It also provides a practical warning about freshness, making it complete for an agent to correctly select and invoke.

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 takes zero parameters and the description adds no parameter information, but per rubric, the baseline for 0 params is 4. The description clearly states the output fields, so it adds meaningful context beyond the empty 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 the specific verb 'Report' and names the resource as 'the sanctions list version,' with explicit details on the fields (source, publication date, hash, size, age). It clearly distinguishes itself from sibling tools like screen_against_sanctions by focusing on version metadata rather than matching or screening.

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 a concrete scenario (an auditor asking for the version) and a critical caution about checking the freshness field before relying on nil screening results. This explicitly guides when to use the tool and warns against misuse, effectively differentiating its purpose from screening tools.

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?

With no annotations, the description carries the full transparency burden and does so thoroughly. It discloses fuzzy matching, transliteration and obfuscation handling, the meaning of a hit, the meaning of a nil result, and the common-name false-positive risk. There is 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?

The description is dense but well structured: a direct first sentence, a clearly marked SCOPE section, and a practical caveat about common names. Every sentence provides compliance-relevant value with no filler or restatement of trivial 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?

For a tool with no output schema and no annotations, this description is unusually complete: it explains what result shape to expect, how to interpret hits, how to interpret nil results, and the compliance risk of overstating results. It gives the agent enough context to invoke the tool and communicate results safely.

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 describes all four parameters at 100% coverage, so the baseline is 3. The description reinforces that partial names are accepted but does not add material meaning about the threshold default or includeWeak beyond what the schema 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 opens with a clear verb and resource: 'check whether... appears on the OFAC SDN list', and adds matching behavior, score output, and list publication date. This clearly distinguishes it from generic entity matching and normalization sibling tools by identifying the exact sanctions list and the compliance purpose.

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 states when to use the tool — OFAC SDN screening — and provides important exclusions: it is not UN, EU, UK, DFAT, or PEP screening, and a nil result is not clearance. It does not explicitly name sibling alternatives, so it stops just short of a full 5.

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?

With no annotations provided, the description carries the full burden and does well by disclosing that it 'Returns the totals plus a comparison of per-line versus total-based rounding and the discrepancy between them.' This adds meaningful behavioral context beyond the schema, explaining the tool's key side-effect-free output and the ATO rule, though it could go further in noting edge cases.

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 concise sentences with effective front-loading. The first sentence states the core function, and the second immediately explains the return value and the rationale, making every word useful with no redundancy or ambiguity.

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 tool with moderate complexity, the description covers the main capabilities (multi-line, GST-free, rounding modes) and the interesting output (comparison/discrepancy), while the schema handles parameter details. It remains a bit thin on edge cases like taxInclusive behavior or default rounding, but these are already documented in the schema, so the description is contextually sufficient for an agent to decide usage.

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. The description does not add new parameter details; it merely reiterates the concept of rounding methods already explained in the schema. It adds no extra semantic value for the 'lines' or 'taxInclusive' parameters, so a 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 uses a specific verb 'Total' with a clear resource 'multi-line invoice' and immediately references GST and rounding methods, which precisely distinguishes it from sibling tools. It clearly states what the tool does and implies its unique value proposition (handling GST-free items and both rounding methods).

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 when to use the tool by describing its function, but it does not explicitly mention alternatives or when not to use it. It does not reference sibling tools or conditions for choosing this over them, so guidance is present only by implication.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains that spaces and hyphens are ignored, that a valid ABN additionally reports company status, that BSBs have no check digit, and that checksum success only catches typos and fabrications. This is unusually transparent about both capabilities and limitations.

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 a concise one-sentence summary of the tool's core function, followed by a short list of formatting behaviors and a clearly separated IMPORTANT caveat. Every sentence earns its place; there is no filler or repetition of 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?

The description covers purpose, scope, formatting rules, ABN-specific behavior, and important limitations, which is strong given the absence of annotations. However, because there is no output schema, the description could have been slightly more explicit about the exact return format or result structure, even though 'reports' gives a reasonable hint.

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%: both parameters already have clear descriptions and the type parameter includes an enum. The tool description adds context about checksum validation and ABN company reporting, but it does not add substantial parameter-specific meaning beyond what the schema already provides, so 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 uses a specific verb and resource: 'Check an Australian ABN, ACN, ARBN, TFN, Medicare card number or BSB against its official checksum.' It clearly distinguishes the tool from siblings like calculate_gst, normalise_entity_name, or screen_against_sanctions by stating exactly what kind of validation this performs.

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 the tool is appropriate and strongly warns against misinterpreting a passing checksum as proof of registration or authenticity. It does not name alternative tools explicitly, but it effectively explains when not to rely on the result, earning a high score.

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