Azure Cost Management MCP

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool targets a distinct aspect of cost management: querying, forecasting, dimensions, alerts, budget CRUD, and Advisor recommendations. The descriptions clarify boundaries between similar tools like alerts vs. budget list, preventing confusion.
Naming Consistency4/5
Names follow a consistent prefix pattern (azure_cost_, azure_budget_, azure_advisor_) with mostly verb-based actions (query, forecast, list, get, create, delete). However, 'azure_cost_dimensions' and 'azure_cost_alerts' use noun forms, which is a minor deviation from the verb_noun convention.
Tool Count5/5
Nine tools is well-scoped for an Azure cost management server. Each tool serves a clear purpose without redundancy, covering the essential operations for cost analysis and budget management.
Completeness4/5
The tool surface covers the core cost management lifecycle: querying, forecasting, alerts, budget CRUD, and recommendations. Minor gaps exist, such as lacking cost export functionality or direct alert configuration, but these are not critical for most workflows.
Average 4.1/5 across 9 of 9 tools scored. Lowest: 3.5/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden for behavioral disclosure. It uses the verb 'Retrieves' implying a read-only operation, but does not disclose any other behavioral traits such as auth requirements, rate limits, data latency, or what happens with invalid scopes. It also does not describe the response format or any side effects. This is a significant gap for a tool with no annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the primary purpose, and then provides a specific usage scenario with a cross-reference to azure_budget_create. Every word earns its place, with no redundant information or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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 should help the agent understand what to expect. It implies the output contains forecast values and enables comparison with budgets, but it does not specify the return structure, granularity behavior, or any limitations. The schema is rich and the use case is clear, but the lack of output details and no annotations leaves the description incomplete for a fully autonomous agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 tool description itself does not add parameter information, but the schema provides detailed descriptions for all parameters, including scope formats and the CAF tip for mg: scope. The description does not need to compensate further, though it does not enhance parameter understanding either.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Retrieves cost forecasts based on current usage trends' with a specific verb and resource. It also provides a use case (WAF Cost Optimization) and references azure_budget_create. However, it does not explicitly differentiate from sibling tools like azure_cost_query, relying on the tool name and context to imply distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
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 this tool: for comparing forecast vs budget to detect overspend risk and for setting budgets with a 100% forecasted threshold to receive early warnings. It also points to azure_budget_create as a complementary tool. While it doesn't state exclusions or alternatives explicitly, the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 explicitly states that deletion is 'Permanent' and 'cannot be undone,' which accurately conveys the destructive and irreversible nature of the operation. This goes beyond a simple 'deletes' statement, providing crucial risk awareness for the agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences that immediately convey the action and the key warning. Every word is purposeful, and the most critical information ('Permanently deletes') is front-loaded. This is a model of concise writing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description, combined with the fully detailed schema, provides adequate context for a simple delete operation. It covers the action and the irreversibility, which is the main caveat, and the parameters are well-documented in the schema. However, it does not mention potential side effects (e.g., impact on cost data) or response expectations, and there is no output schema to fill that gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides 100% parameter description coverage, including detailed examples and context for 'scope' and a clear definition for 'budgetName'. The tool description adds no additional parameter semantics, so the baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('deletes') and resource ('cost budget'), making the action unambiguous. It clearly distinguishes this tool from sibling tools like azure_budget_list, azure_budget_get, and azure_budget_create by indicating a destructive operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage (when you want to remove a budget) but does not explicitly state when to use this tool versus alternatives, nor does it mention any prerequisites or exclusions. The irreversibility warning serves as a caution, but there is no explicit 'when-to-use' or 'when-not-to-use' guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It specifies that the tool returns current spend, thresholds, and alert status, which implies a read-only list operation. However, it does not explicitly state safety, error behavior, pagination, or prerequisites, leaving some gaps for the agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences: the first states purpose and return details, the second adds practical usage context (WAF/CAF). No wasted words, and key information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is a list operation with a single well-documented parameter and no output schema. The description covers the return content and gives usage scenarios, which is reasonably complete. It does not mention pagination or handling of empty results, but for a list tool of this simplicity, the provided information is largely sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides 100% coverage of the 'scope' parameter with examples and CAF guidance, so the description adds no additional parameter semantics beyond what the schema offers. Given the high schema coverage, the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool 'lists all cost budgets at a given scope' with a specific verb and resource, and includes the return fields (current spend, thresholds, alert status). This distinguishes it from siblings like azure_budget_get (single budget) and azure_budget_create/delete, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage context with WAF Cost Optimization and CAF scenarios, explicitly stating when this tool should be used for budget coverage verification. It does not explicitly mention alternative tools or exclusions, but the context is sufficiently clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the transparency burden. It uses a non-mutating verb ('Queries'), specifies cost type options, and outlines grouping/time-filtering behavior. It does not detail return format or rate limits, but for a read-only query this is reasonably transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences: first states the core function, second/third provide WAF/CAF use cases. Every sentence adds value with no fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For an 8-parameter tool without output schema or annotations, the description plus schema provide a solid understanding. It communicates the main purpose, use scenarios, and required scope information. Minor omission: it does not describe the output shape (e.g., aggregated rows) or default aggregation, but the schema's granularity/groupBy defaults partially cover this.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, including detailed parameter descriptions for scope formats, groupBy tag syntax, and enum meanings. The prose description adds only high-level mentions of grouping and time filtering, not additional parameter-level semantics, so baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The opening sentence uses the specific verb 'Queries' and identifies the resource ('actual or amortized costs for an Azure scope') with key capabilities ('grouping and time filtering'), clearly distinguishing it from sibling tools like azure_cost_forecast or azure_cost_dimensions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides concrete usage scenarios: WAF Cost Optimization for identifying top spenders, tracking daily trends, and chargeback/showback via tags; and CAF usage with management group scope for cross-subscription reporting. It does not explicitly name alternative tools, but the use cases imply when cost query is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of disclosing behavioral traits. It explicitly mentions defaults ('Default notifications: 80% actual, 100% actual, 100% forecasted'), the upsert behavior, and WAF/CAF best practices. However, it does not mention required RBAC permissions or potential side effects of overwriting an existing budget beyond 'updated in-place,' so it's not fully transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is informative but not excessively verbose. The first sentence is direct, and the WAF/CAF sentences provide useful guidance. The structure is logical (what → defaults → best practices → upsert behavior). It could be slightly tighter, but every sentence contributes to the tool's effective use.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 11 parameters and no output schema, the description covers the critical aspects: default thresholds, WAF/CAF alignment, and upsert semantics. It doesn't describe return values or error conditions, but the core behavioral context is well covered. Overall, it's complete enough for an agent to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already documents all parameters. The description adds value by explaining WAF-recommended thresholds ('80% actual, 100% actual, 100% forecasted') and CAF guidance for timeGrain and contactRoles, giving semantic context beyond the raw schema. The in-place update note clarifies the combined behavior of name and scope.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Creates or updates a cost budget' with a specific verb and resource, distinguishing it from sibling tools like azure_budget_list and azure_budget_delete. It also explains the upsert behavior ('If a budget with the same name already exists at the scope it will be updated in-place'), making the purpose and behavior unmistakable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear WAF and CAF context, advising when to set actual and forecasted alerts ('always set both actual and forecasted alerts') and how to align timeGrain with billing cycles. It doesn't explicitly exclude alternatives, but the create/update scope is clear relative to siblings. The in-place update note also guides usage for existing budgets.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It explicitly states this is a retrieval operation and enumerates the exact data returned (spend, thresholds, filters), which is transparent. It does not, however, describe error cases or permissions, but the read-only nature is clear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with no filler; verb-first structure immediately conveys the action and scope of results.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple 2-parameter GET tool with no output schema, the description sufficiently specifies what is returned and the target budget. No additional context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, with detailed descriptions for both parameters, especially scope which includes CAF guidance. The tool description itself adds no parameter-specific meaning, so baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Retrieves') and identifies the resource ('a specific budget') and the details returned ('current and forecasted spend, all alert notification thresholds, and filter configuration'). It clearly distinguishes from sibling list/create/delete tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies when to use this tool—when full details of a specific budget are needed—but does not explicitly mention alternatives or exclusions. The phrase 'specific budget' provides clear context for choosing this over azure_budget_list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the transparency burden. The verb 'Lists' clearly implies a read-only operation. It adds context about the types of alerts and the WAF purpose. While it doesn't mention pagination, auth, or side effects, for a list tool the description effectively conveys non-destructive behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the core function, then a relevant WAF context and sibling compliment. Every sentence earns its place, with no redundant phrasing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter list tool with no output schema, the description is remarkably complete. It covers what alerts are returned, the scope, a usage scenario, and the relationship to a sibling tool. No critical gaps for an agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the scope parameter is fully described in the schema. The description adds no additional parameter-level detail, sticking to the baseline of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists active cost alerts for a scope, enumerating alert types (budget breaches, credit warnings, quota violations, anomalies). It uses a specific verb+resource+scope structure and distinguishes itself from sibling azure_budget_list by focusing on alerts rather than budgets.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear use case: 'monitor alerts to detect unexpected cost spikes before they escalate.' It explicitly recommends complementing with azure_budget_list to correlate alerts with budget thresholds. It does not explicitly state when not to use it, but the context and sibling reference give adequate guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, but the description's verb 'Lists' clearly indicates a read-only operation. It adds practical context about discovering tag keys for WAF analysis. Since it is a simple list operation, this level of transparency is adequate, though it stops short of stating permission needs or pagination behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, both information-dense. The first states the core functionality, the second provides cross-tool usage guidance and a WAF tip. No filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has only one parameter, no output schema, and low complexity. The description adequately covers what it does, how it relates to azure_cost_query, and a practical use case. It is complete for an AI agent to select and invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with a detailed explanation of the scope parameter. The tool description does not add additional parameter semantics, which is acceptable given the schema already covers it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool's action ('Lists available dimensions') and resource ('cost grouping and filtering at a given scope'). It also distinguishes itself from sibling azure_cost_query by noting it provides valid values for the groupBy parameter.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance on when to use the tool: 'Use this to discover valid values for the groupBy parameter in azure_cost_query.' It also includes a WAF tip for chargeback/showback analysis. However, it does not mention when not to use it or present alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the burden. It discloses the scope restriction (subscription only, not management group) and what types of recommendations are covered (unattached disks, unused IPs, etc.). It implies read-only behavior with 'Retrieves,' but does not disclose rate limits, authentication, or return format. Still, the scope and coverage details are valuable behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, each earning its place: purpose, coverage scope, and usage/scope caveat. The description is front-loaded with the primary action and resource, and wastes no words. Structure is clear and scannable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity and lack of output schema, the description sufficiently covers what it does, when to use it (WAF/FinOps), and scope limitations. It stops short of describing pagination or response structure, but for a retrieval tool this is acceptable and covered by the parameter schema for top/limit.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for all three parameters, so the baseline is 3. The description adds meaningful context beyond the schema: it explains which recommendations are included (right-sizing, idle resources) and explicitly advises starting with High impact for maximum savings, reinforcing the impact parameter. This adds practical meaning to the parameter choices.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Retrieves' and the resource 'Azure Advisor cost optimization recommendations for a subscription.' It distinguishes from sibling tools like cost queries or forecasts by focusing specifically on Advisor recommendations and even lists example categories (right-sizing VMs, deleting idle resources).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance: 'WAF Cost Optimization: address High impact recommendations first' and 'CAF FinOps: run across all landing zone subscriptions.' It also sets a hard constraint by noting 'Advisor operates at subscription scope; management group scope is not supported.' However, it does not explicitly compare to alternatives like azure_cost_query or azure_cost_forecast, though the context makes the intended use clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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