Vietnis Cloud Cost Calculator

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so safety is covered. The description adds real context beyond that: the price basis is pay-as-you-go list price and the default region set is documented, which tells the agent what data it will actually get. It stops short of noting currency, whether discounts/reserved pricing are excluded, or data freshness.

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 that names the operation and scope before adding the default-region detail and the rationale. The trailing 'because the same SKU is priced differently' clause is mildly redundant but justifies why the tool exists, so it mostly 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?

There is no output schema, so the description should indicate what comes back — it does not say whether results are a per-region price list, sorted, or in what currency. Combined with unmentioned os/lang parameters, an agent could invoke this correctly for a basic comparison but would miss important knobs and expectations.

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

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

Schema description coverage is 0%, so the description must carry the parameter burden. It explains the regions default well and 'size' is self-evident, but it never mentions 'os' (linux/windows) or 'lang' (en/vi) despite OS licensing being a major driver of Azure price differences — a significant omission for a pricing-comparison tool.

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 resource: compares pay-as-you-go list price of one Azure VM size across several regions. The 'across several regions' scope implicitly distinguishes it from the single-region estimate_azure_vm sibling, but it never names that sibling or explicitly contrasts the two.

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 only implied: an agent can infer this is the tool for multi-region price shopping versus a single estimate, but there is no explicit when-to-use/when-not-to-use guidance. The default region set (nearest Vietnam plus East US) is mentioned, which hints at the typical use case, but alternatives like recommend_cloud_region are not referenced.

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?

Read-only and open-world behavior is already carried by the annotations, so the description's job is lighter. It does add useful disclosure about what comes back (each provider's market list price plus the Vietnis price), but says nothing about whether prices are per-seat or total, currency handling, or how 'comparable' plans are matched.

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 that states the core capability first. The parenthetical enum list adds length without adding much, but there is 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?

With no output schema and five undocumented parameters, the description leaves significant gaps for a pricing tool: billing cycle semantics, language of results, and whether prices are per-user per-month are all unstated. It partially covers level and country, but not enough 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 description coverage is 0%, so the description must carry parameter meaning. It explains users, country and the four level values, but omits billing (annual vs monthly) and lang entirely — billing is a material pricing driver for a cost-comparison tool.

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 (side-by-side cost comparison), the two named resources (Google Workspace vs Microsoft 365), and the scope (comparable plans, a user count, one country). An agent can tell this apart from generic siblings like estimate_saas_cost, though it never explicitly contrasts 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 only implied: the description makes clear this is for two-vendor comparison at a given seat count and country, but it gives no explicit when-to-use versus estimate_saas_cost, no prerequisites, and no exclusions.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so safety is covered. The description adds genuine context by naming the upstream source ('Azure Retail Prices API for that region and SKU'), implying live external data and possible availability gaps by region/SKU. It does not disclose failure behavior for unknown SKUs, caching, or how reserved/spot rates are surfaced given no parameter selects 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 sentences, front-loaded with the core purpose, but the parenthetical pricing-mode list and the opaque 'Vietnis price and VND' clause consume space without adding actionable meaning. Tightening toward what the tool takes and returns would improve it.

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?

There is no output schema, and the description does a fair job summarizing what comes back (list price, monthly cost for N machines, converted VND figure). Given six parameters and an external data source, the missing pieces are the unselected pricing modes and the undocumented os/lang/hours parameters, but the essential shape is conveyed.

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?

With only 33% schema description coverage, the description needs to carry parameter meaning, and it only partially does: it clarifies that cost is computed for N machines (the count parameter). The os, lang, and hours_per_month parameters are unexplained in both description and schema, and the description advertises 1-/3-year reserved and spot pricing even though no parameter exists to select a pricing mode, leaving an agent unable to reconcile inputs with outputs.

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 pairing ('Live Azure list price for a VM size in a specific Azure region') and enumerates the pricing dimensions returned, which separates it from estimate_gcp_vm. The unexplained phrase 'the Vietnis price and VND' introduces ambiguity about an extra output, and no sibling tool is named explicitly, keeping it below a 5.

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

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

Usage is only implied by the listed outputs: an agent can infer this is the single-VM-size pricing call, distinct from compare_azure_regions or recommend_cloud_region. There is no explicit 'use this when…' statement, no exclusions, and no guidance on which sibling to pick for multi-region comparison.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds that these are list prices from the Cloud Billing Catalog API, which usefully signals non-negotiated pricing, but it says nothing about live vs cached data, latency, or what the cryptic 'Vietnis price' refers to.

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 short and front-loads the resource being priced, but the second sentence is awkward and the undefined term 'Vietnis price' spends words without conveying meaning.

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 five-parameter estimation tool with no output schema, the description should say what the response contains (monthly total, currency, per-hour breakdown) and how count/hours_per_month affect it. Neither is addressed, leaving the agent guessing at the return shape and the semantics of two cost-driving parameters.

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

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

Schema coverage is only 40%, and the description does not compensate: three of five parameters (lang, count, hours_per_month) receive no explanation anywhere, even though hours_per_month and count directly determine the estimate total. Region and machine_type examples merely duplicate the schema text.

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+resource (list price for a machine type in a GCP region) and names the underlying data source (Cloud Billing Catalog API). It is distinguishable from estimate_azure_vm by the GCP focus, though no sibling is named explicitly and the phrase 'Vietnis price' is unexplained.

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?

There is no when-to-use or when-not-to-use guidance and no reference to alternatives such as recommend_cloud_region, compare_azure_regions, or list_cloud_offerings. An agent must infer from the name alone that this is the GCP VM pricing path.

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 only cover readOnlyHint and openWorldHint; the description adds genuinely useful behavior beyond them, namely that pricing uses each country's list price because prices differ by market, and that output includes the Vietnis price and a VND conversion. It does not disclose what happens for unsupported SKUs/countries or which currency the primary figures are in.

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 that leads with the verb and output, with no filler. It is dense and slightly crammed at the end ('plus the Vietnis price and a VND conversion'), but every clause carries 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?

With no output schema, the description carries the burden of describing returns and does so adequately by naming monthly/annual cost, Vietnis price, and VND conversion. Minor gaps remain around unsupported-input handling and output currency, but the essentials for correct invocation are 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 coverage is 50%, with country and billing well documented in the schema and sku documented via a list_cloud_offerings reference. The description adds the market-dependent pricing rationale for country but says nothing about lang, and adds no detail on billing commitment semantics or how multiple items are aggregated, so it only marginally exceeds 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 states a specific verb ('Estimates') plus the resource ('monthly and annual licence cost for one or more Google Workspace or Microsoft 365 SKUs') and scope ('in a given country'). It is clear what the tool produces, but it does not explicitly distinguish itself from the sibling compare_workspace_vs_m365, which also computes cost-related output.

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 only implied: an agent can infer this is the tool for pricing SKUs, and the schema's sku description points back to list_cloud_offerings as the source of IDs. There is no explicit when-to-use, when-not, or named alternative (e.g., when to use compare_workspace_vs_m365 instead).

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 and openWorldHint=false, so the safety profile is covered. The description adds real business context beyond that – VAT invoices in VND, Vietnamese-language support, and the product lines covered – but says nothing about response shape, whether data is static or fetched live, or how the lang option changes the output.

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 dense sentence with the resource named first and supporting detail (currencies, VAT, language, product lines) packed into parentheses. Nothing is redundant, though the parenthetical product list is slightly heavy for a contact-lookup tool.

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?

This is a simple, single-parameter, read-only lookup with no output schema, and the description covers the essentials of what the tool is about. It is still incomplete on two points an agent would want: what the returned contact/service data actually contains, and the effect of the only parameter.

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

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

Schema description coverage is 0% for the single 'lang' parameter, so the description must carry the burden and largely does not. The phrase 'Vietnamese-language support' hints at a language dimension, but it never states that 'lang' selects the language of the returned information or what 'en' versus 'vi' yields.

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 the specific resource (Vietnis, a Vietnam-based reseller) and enumerates what it exposes: contact details and services. It also ties itself to the sibling estimation tools by noting its prices appear in 'these estimates', which orients the agent. It falls short of a 5 only because it doesn't explicitly contrast itself with any specific sibling tool.

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 only implied: an agent can infer this is the lookup to call when a user asks who the reseller is or wants to contact them after seeing an estimate. There is no explicit statement of when to call it, when not to, or what to do instead, and no mention of whether it should be called before or after an estimation.

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 and openWorldHint=false, so the safety profile is covered. The description adds useful scope context — that only countries with verified list prices appear, and that Azure regions are live while Google Cloud regions are only sources. It does not address freshness/caching or result size, which keeps it at a 3.

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, but the trailing "and ... and ..." chain makes it a dense run-on that mixes output contents with data-quality caveats. Slightly restructured, it could be crisper, but nothing is wasted.

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, two-optional-parameter listing tool with no output schema, the description conveys what the agent will get back reasonably well. However, it omits any explanation of how the parameters change the result, which is the main remaining ambiguity for invoking 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?

Schema description coverage is 0%, so the description should carry the burden for the two parameters (lang, provider). It never mentions either, leaving the agent to interpret the enums alone and to guess whether provider filters SKUs, price sources, or both. The one useful hint — that SaaS SKUs are Google Workspace and Microsoft 365 — only loosely maps to provider.

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 ("Lists") and enumerates the resources returned: SaaS SKUs, countries with verified list prices, infrastructure price sources, and discount tiers. That clearly separates it from the sibling estimate_*/compare_* tools, which compute rather than enumerate. It stops short of naming a sibling outright, so a 4 rather than a 5.

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

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

There is no when-to-use guidance, no mention of alternatives such as estimate_saas_cost or recommend_cloud_region, and no statement of prerequisites. An agent must infer that this is a discovery/catalog step before the estimation tools. 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?

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered and the bar is lower. The description adds a useful domain fact (neither Azure nor GCP has a local Vietnam region) but says nothing about how 'closest' is determined—geography, latency, or hardcoded mapping—nor what the result contains.

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 compact sentence with the core purpose front-loaded and the parenthetical fact placed where it supports rather than obscures. The trailing 'useful before pricing infrastructure' is slightly loose but earns its place as the only usage signal.

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, read-only lookup with no output schema, the description conveys the essential shape of the result ('lists ... regions closest'). It falls short on parameter documentation and on what a returned region entry looks like, which matters given the total absence of schema descriptions.

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

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

Schema description coverage is 0%, so the description must carry the parameter burden. It only implicitly touches the country parameter via the Vietnam/VN default example; the 'lang' enum (en/vi) is never mentioned, and there is no guidance on country format (ISO code vs. name) or how language affects output.

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 resource: 'Lists the Azure and Google Cloud regions closest to a country', naming both providers and the proximity criterion. This distinguishes it from siblings such as compare_azure_regions and the estimate_* pricing tools, though the name's 'recommend' framing is only loosely reconciled by the description's 'closest' wording.

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?

'useful before pricing infrastructure' implies a workflow position ahead of the estimate_* tools, which is genuine usage context. However, no alternative tool is named explicitly and there are no exclusions or preconditions, so the routing guidance remains 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.