Dubai Log

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

With no annotations provided, the description carries the full behavioral burden, and it delivers two concrete traits: the operation is deterministic arithmetic with no AI involved, and unpublished inputs yield 'not_published' rather than a fabricated number. That fallback behavior is genuinely useful for an agent reasoning about trustworthiness. It does not state auth requirements or the shape of the returned value, which keeps it out of the top tier.

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

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

Three sentences, front-loaded with the core action and scope, followed by the kind enumeration and the behavioral caveat. The dense kind list earns its length by mapping opaque enum values to user-facing concepts; there is 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 10-parameter tool with 1 required field, no output schema, and no annotations, the description covers purpose, kind semantics, and failure behavior adequately. The main remaining gap is that conditional parameter requirements per kind are only partially documented (in the schema descriptions, not the tool description), and the return value shape is only hinted at via 'not_published'.

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 80%, so the baseline is 3, but the description adds real meaning beyond the schema by explaining what each 'kind' enum value means and, more importantly, which parameter family applies to which kind (e.g., taxi_minimum relates to the RTA minimum fare, salik_monthly to a commute toll). This resolves ambiguity the raw enum labels leave open. It does not document the fuel enum or the conditional requirements linking each kind to its parameters.

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 ('Compute') and resource ('one Dubai cost from published rates'), then enumerates all four supported cost kinds with a plain-language gloss for each. An agent immediately knows the tool's scope. It stops short of explicitly differentiating itself from the sibling get_rates, leaving that contrast to inference.

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 per-kind mapping ('taxi_minimum (RTA minimum fare)', 'salik_monthly (toll cost for a commute)') implicitly tells the agent which kind to pick for a given question. However, there is no explicit when-to-use vs get_rates, no statement of prerequisites, and no exclusions. Usage is implied 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. It helpfully discloses provenance behavior ('every rate carries its source URL and the date it was read'), which is real value beyond structured fields. It does not state read-only nature, caching/freshness semantics, or whether rates are live versus published snapshots.

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 zero filler, front-loading the resource and enumerating the three rate categories before adding the provenance detail. Every clause 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?

With no output schema, the description must cover return values and does so well by naming the three rate families and the provenance fields. It falls short of full completeness only on data format and freshness/liveness, which an agent might still wonder about.

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 there is no parameter semantics to clarify; baseline 4 applies. No syntax or filtering detail is needed or expected.

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 ('Return') and a specific resource (the Dubai/UAE rate card with RTA minimum fares, Salik toll rates, fuel prices), so the agent knows exactly what data arrives. It does not explicitly differentiate itself from siblings like compute_cost or list_sources, leaving the boundary to inference.

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 'this service computes with' implies this is the data behind compute_cost, giving implied usage context. However, there is no explicit when-to-use, when-not-to-use, or named alternative 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?

No annotations are provided, so the description carries the full burden. It discloses the output (up status and version) and implies a read-only operation, but does not explicitly state side-effect safety, idempotency, authentication needs, or rate limits. For a simple zero-parameter health check this is adequate but not rich.

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, front-loaded with what the tool reports and then when to use it. Every sentence earns its place with no redundant or filler 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 tool is very simple: no parameters, no output schema, no annotations. The description covers purpose, output, and usage context sufficiently. Explicit failure or error behavior is not described, but that is a minor gap for a health check.

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 baseline of 4 applies. The schema has no properties and coverage is trivially 100%; the description needs no parameter details and provides none.

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 'Report' and the resources 'service is up' and 'version answered'. Clearly distinguishes itself from siblings compute_cost, get_rates, and list_sources, which serve unrelated purposes. An agent can identify this as a health/status probe without opening the 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?

Explicitly says to use it to confirm the endpoint is reachable before running a real job, giving a clear precondition. No when-not or alternative tools are named, but the sibling tools are not alternatives. Context is clear but not 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?

No annotations are provided, so the description carries the full burden, and this is a zero-parameter read-only listing with minimal risk. It discloses the shape of the result (source URL plus the date it was read), which is useful freshness context, but says nothing about coverage, ordering, or whether the list can be empty.

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 short sentences, zero filler, with the what (list sources + fields) and the when (before quoting a number) both front-loaded.

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 no-param, no-output-schema tool, the description adequately covers what comes back (URL and read date) and when to call it. Slight gap: no indication of the list's completeness or ordering, but nothing an agent strictly needs in order to invoke 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?

The tool takes zero parameters, so the baseline is 4 by rule. The description adds nothing needed here, and none is missing.

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 (List) and resource (the primary sources behind this service's rates), and even previews the returned fields (URL and read date). It implicitly contrasts with get_rates by focusing on provenance rather than the rates themselves, 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?

Gives an explicit trigger: "Use it before quoting a number to a user." That is a clear when-to-use condition, though it offers no exclusions or named alternatives (e.g., when to skip straight to get_rates).

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