melb.si data for agents

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

No annotations are provided, so the description carries the burden; it does disclose a meaningful trait by stating the answer comes from 'the publisher's own information', signaling self-reported/possibly promotional provenance. It does not say whether the result is static, cached, or how it's structured, leaving gaps for an annotation-free 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?

A single front-loaded sentence with no filler; every clause (operator, services, data policy, provenance) adds distinct 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 simple zero-param informational tool with no output schema, the description covers the subject matter an agent needs to decide and call correctly. Slightly more guidance on output shape or scope boundaries would make it complete, but nothing critical 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 tool takes zero parameters, so the parameter baseline of 4 applies; there is nothing for the description to disambiguate.

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 (melb.si's operator, services, and data policy) and a specific verb (explain), so the agent knows exactly what kind of content comes back. It is distinguishable from siblings like vic_public_holidays or melbourne_ai_events, though it does not explicitly contrast itself with them.

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?

Usage is implied by the topic — call this when provenance, ownership, or data-policy questions about melb.si arise — but there is no explicit when-to-use statement, no exclusions, and no mention of what to use instead for related queries.

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 behavioral burden. 'Find' implies a read-oriented retrieval tool and the description usefully discloses that confirmed/inferred status, sources, and checked dates are preserved, but it does not explicitly confirm read-only behavior, permissions, pagination, or result shape.

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 with no wasted wording. It is concise and puts the core resource first, though the semicolon clause could be slightly more structured for scanning.

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 three-parameter retrieval tool with fully documented parameters, the description is adequate. However, with no annotations and no output schema, it does not fully explain the result structure or how it relates to the similar Victorian sibling 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?

Schema description coverage is 100%, so the schema already documents lang, query, and topic. The description adds no parameter-specific meaning beyond the schema, 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 gives a clear verb and resource: find curated Australian small business AI rules and guidance. It adds relevant scope around confirmed versus inferred status, sources, and checked dates, making the tool distinguishable in general, though it does not explicitly contrast with the similar sibling vic_ai_help_for_small_business.

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 does not state when to use this tool, when not to use it, or which sibling to choose instead. Given the close sibling vic_ai_help_for_small_business, this leaves important routing guidance 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?

With no annotations, the description must carry behavioral context. It discloses that results include sources and checked dates, but lacks details such as read-only status, rate limits, auth requirements, or data freshness beyond the vague 'checked dates'.

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 with no wasted wording. It fits the tool's simplicity well.

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, all parameters are documented, and the description says results include sources and checked dates. It does not fully explain return shape or sort/limit behavior, but no output schema exists and the core invocation context is 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%, so the parameters are already documented. The description implies date filtering but adds no syntax or meaning beyond the schema for 'to', 'from', or 'lang'.

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') and resource ('upcoming Melbourne AI events') with a clear date scope. It does not explicitly distinguish itself from sibling tools, but the purpose is still 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?

It gives a temporal scope ('from today in Melbourne or a later date') but does not say when to use this tool versus alternatives or when not to use it. The intended use is inferable, but there is no explicit 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?

With no annotations, the description carries the burden and does substantial work: it discloses the result cap ('up to five'), the ranking mechanism (word/substring matches in questions, aliases and answers), and that answers come with official sources and checked dates. It omits what happens on zero matches and whether the lookup is purely read-only, but the core behavioral picture is present.

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, zero waste, with the domain and examples front-loaded and the return contract second. Every clause carries information the agent can act on.

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, read-style lookup with no output schema and no annotations, the description covers scope, ranking and provenance adequately. The main gap is the no-result behavior and any hint about answer freshness limits, though 'checked dates' partially addresses the latter.

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 both query and lang are already documented in the schema. The description adds only indirect signal — mentioning Chinese substring matching hints at the multilingual lang option but never names it or explains the en/zh/both tradeoff. Baseline 3 is appropriate when the schema does the heavy lifting.

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?

States a specific verb and subject domain ('Answer everyday questions about living in or visiting Melbourne and Victoria') and backs it with concrete topic examples (myki, parking signs, rental bonds), so the agent knows exactly the kind of query it serves. It does not explicitly differentiate from the sibling about_melbsi, which is the one plausible source of confusion.

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 enumerated topic list gives a clear sense of the query domain and implies when to route here rather than to the business-rules or events siblings. There are no explicit exclusions or named alternatives for cases like legal advice or non-Victorian questions, so the boundary is inferred rather than 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?

No annotations are provided, so the description carries the burden; 'Find' plus 'sources and checked dates' implies a read-only curated lookup and usefully signals provenance/freshness metadata. It does not state what happens with no matches, whether an empty query returns everything, or any pagination/result-size behavior.

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?

One sentence, front-loaded with the verb and scope, and the trailing 'including eligibility, cost, sources and checked dates' earns its place because no output schema exists to convey returned fields. 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 and no annotations, the description does the important work of naming the returned fields, which is what an agent needs here. The remaining gap is behavioral: with zero required parameters it is unclear whether an empty call lists everything or returns nothing.

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% and both parameters are fully documented in-schema (query substring semantics, lang enum with preserved source links), so the description need not compensate. It adds no parameter-level meaning beyond the schema, which is the baseline-3 case for two fully documented params.

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?

States a specific verb (Find) and resource (curated AI training and assistance) with a clear geographic/audience scope (Victorian small businesses), and even enumerates the returned fields. It implicitly separates itself from au_small_business_ai_rules and melbourne_ai_events by being a training/assistance directory rather than a rules or events source, but it never names a sibling explicitly.

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 inferable from the scope (Victorian small businesses seeking AI training) but there is no explicit when-to-use statement, no exclusions, and no pointer to the closely related au_small_business_ai_rules sibling for regulatory questions. The agent must deduce routing from the scope wording alone.

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 burden. It does add genuine behavioral context that structured fields lack: results come with official sources and checked dates, and uncovered years return unknown rather than fabricated data. It omits return shape, behavior with conflicting/absent parameters, and any failure modes.

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?

A single front-loaded sentence with the lookup modes stated first and the provenance/coverage caveat second. No filler, nothing repeated from the schema.

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 two-parameter, no-annotation, no-output-schema tool, the description covers purpose, provenance and coverage limits adequately. But with zero required parameters, the agent is left guessing what an empty call does, so the definition is not fully self-sufficient.

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 already documents both parameters and their formats. The description still adds meaning by framing year and date as alternative lookup modes, which the schema does not express; it does not address precedence when both are provided.

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 pairs a specific verb ("Find") with a specific resource ("Victorian public holidays") and states the two lookup modes, year or exact date. Sibling tools are all unrelated topical assistants, so no differentiation is needed, though none is offered explicitly.

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?

"by year or exact date" implies the two calling modes but never states when to prefer one over the other, nor what happens if both are supplied or neither is supplied (both parameters are optional). Usage is inferable but not spelled out.

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