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

With no annotations, the description carries the full burden. It discloses that results are sorted by price (cheapest first) and that omitting regions shows all available regions. However, it doesn't mention read-only status, potential side effects, or error behavior, so it's somewhat limited.

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 opens with a one-sentence purpose, includes the sorting note, then a clear Args list. Each parameter description is concise and necessary. 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?

The tool has an output schema, so return format is covered elsewhere. All 6 parameters are explained with semantics and defaults. The description covers core behavior (sorting, region default). It lacks only usage guidance vs siblings, but that's a separate dimension. Overall quite complete for a query 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 has no descriptions (0% coverage), so the description fully compensates. It explains each parameter with examples: service_name (e.g., 'Virtual Machines'), arm_sku_name (exact match), sku_name (display name), product_name (more specific filtering), regions (with default behavior), currency_code (default USD). This provides clear meaning 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?

Description states 'Compare prices for an Azure service/SKU across multiple regions' – a specific verb and resource. It clearly distinguishes from sibling tools like search_prices (single price) or list_regions (just listing regions) by focusing on cross-region price comparison.

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?

No explicit guidance on when to use this tool versus alternatives like search_prices or estimate_cost. The use case is implied by the description, but there is no 'use this when' or 'for X, use Y' statement.

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 explains the calculation formula ('multiplies the unit retail price by the quantity and hours') and the special case for flat monthly rates, which adds behavioral context. However, it does not disclose other traits such as whether the estimate includes discounts, taxes, or if it is a read-only operation.

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 well-organized: a one-sentence purpose, a short formula explanation, a practical special-case note, and a bulleted parameter list. Every sentence adds value with no 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?

Given the tool's complexity (7 parameters, multiple SKU/region combinations), the description covers the key inputs and usage nuances, including how to handle storage pricing. An output schema exists, so return values need not be described here. It is nearly complete, though it could mention that prices vary by region or that SKU matching may require precise naming.

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 description adds substantial meaning to every parameter, including clear differentiation between arm_sku_name ('ARM SKU name for exact match') and sku_name ('SKU display name to narrow down'). It also explains the semantics of hours_per_month with a concrete example, which the schema alone does not provide. This fully compensates for the 0% schema description coverage.

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 starts with 'Estimate monthly cost for an Azure service based on quantity and usage hours', which clearly states the tool's specific function. It distinguishes itself from siblings like search_prices by focusing on cost estimation rather than price lookup.

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 practical usage hints, such as 'For storage or data services priced per GB/month, set hours_per_month to 1', but it does not explicitly compare to sibling tools or mention when to prefer this over search_prices, list_services, etc. The context is implied but not 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?

With no annotations provided, the description carries full burden for behavioral disclosure. The verb 'List' implies a read operation, but the description doesn't explicitly state the operation is safe/read-only, nor does it disclose any response format, pagination, or error behavior. It adds minimal information beyond the basic purpose.

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, front-loaded with the purpose, and includes an args section that is directly useful. Every sentence earns its place, with no redundancy or fluff. The structure is ideal for a simple 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?

Given the tool's simplicity and the existence of an output schema, the description is minimally sufficient. It covers the core purpose and parameter semantics, but lacks any extra context such as typical use cases, comparison with siblings, or explicit read-only confirmation. It is adequate but has noticeable gaps for a tool without annotations.

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 provides only the parameter name/type (service_name, string/null, default null) with 0% schema description coverage. The description meaningfully explains the parameter's role: 'If provided, only show regions where this service is available.' This compensation is precise and helpful.

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 'List available Azure regions,' which is a specific verb+resource statement that clearly distinguishes this tool from its siblings (search_prices, estimate_cost, list_services, compare_regions). It also adds a useful qualifier about filtering by service_name, making the behavior explicit.

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 listing Azure regions but provides no explicit guidance on when to use this tool over alternatives like compare_regions or list_services. There is no mention of exclusions or context. The purpose is clear enough to infer, but the tool lacks any direct 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 provided, the description carries the full burden. It discloses the search parameter's case-insensitive substring behavior, but does not mention pagination, result limits, ordering, or whether it returns only service names or additional metadata. This is a gap for a list operation.

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 succinct: a one-sentence purpose and a clearly formatted args block. Every piece of text adds value, with no 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?

For a simple tool with one optional parameter and an output schema, the description is reasonably complete. It covers the core purpose and parameter semantics, and the output schema handles return values. However, it does not address how this tool fits into the larger pricing API workflow, though that is not critical for basic use.

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%, but the description compensates by thoroughly explaining the only parameter 'search', including its optionality and case-insensitive substring matching. This adds meaningful value beyond the schema's type/default info.

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: 'List available Azure services in the pricing API.' It uses a specific verb and resource, and is distinct from sibling tools like list_regions (regions) or search_prices (prices).

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 (when you need a list of Azure services) but provides no explicit guidance on when to prefer this tool over alternatives like search_prices or estimate_cost. It does not mention any exclusions or integration context.

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 full responsibility for behavioral disclosure. It adds useful details like max_results caps and the exact-match semantics of arm_sku_name, but it does not explicitly state that the tool is read-only, how results are returned, or potential rate limits/side effects. This is a moderate level of transparency.

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 well-structured with a one-sentence summary followed by a clean Args list. Every line is informative, using brief but concrete examples, with no fluff or repetition. It is appropriately sized for an 8-parameter search 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?

The description thoroughly documents all parameters and the main purpose, and an output schema exists so return specifics are not needed. However, it lacks guidance on when to choose this tool over sibling tools (e.g., estimate_cost, compare_regions), which is a notable gap given the sibling set and absence of annotations.

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%, but the description compensates richly. Every parameter is documented with examples and clarifications (e.g., 'Use arm_sku_name for exact VM matching', price_type options, currency examples). This adds substantial meaning beyond the bare schema properties.

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 'Search Azure retail prices by service, region, SKU, or product name' — a specific verb and resource with explicit filter criteria. It distinguishes itself from sibling tools (estimate_cost, list_services, etc.) by focusing on searching prices rather than estimating, listing, or comparing.

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?

No guidance is provided on when to use this tool versus alternatives like estimate_cost or compare_regions. The description explains what the tool does but does not offer exclusions, prerequisites, or alternative tool recommendations, leaving the agent to infer usage solely from the name.

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