QA Copilot AI

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes: ping for health, analyze_failure and analyze_report both deal with failures but one is general and one is report-specific, classify_failure categorizes, suggest_fix provides fixes, detect_flaky_tests is unique, and generate_* tools are clearly separate. Minor overlap exists between analyze_failure and classify_failure, but descriptions reduce ambiguity.
Naming Consistency5/5
All tool names follow a consistent snake_case verb_noun pattern (e.g., analyze_failure, generate_test_cases, summarize_bug). Even ping is a verb, and the absence of object is acceptable for a health check. No mixing of styles or vague verbs.
Tool Count5/5
With 10 tools, the server is well-scoped for an AI QA assistant. Each tool addresses a specific need—health check, failure analysis, report parsing, fix suggestion, classification, flaky detection, and three generation tools plus bug summarization—without unnecessary redundancy.
Completeness4/5
The domain of QA assistance is well-covered: generating test cases, scripts, and API tests; analyzing and classifying failures; suggesting fixes; detecting flaky tests; and summarizing bugs. A minor gap is lack of explicit test maintenance or planning tools, but the core lifecycle of creating and debugging tests is solid.
Average 3/5 across 10 of 10 tools scored. Lowest: 2.1/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 7 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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

Behavior1/5
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 only states 'Analyze test failure using AI' without mentioning any side effects, return behavior, external calls, or required permissions. Users are left completely in the dark about what the tool actually does at runtime.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence, which is concise, but it is under-specified rather than appropriately brief. It omits essential details about usage and behavior, making it more of a placeholder summary than a helpful tool description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has one parameter, no output schema, and no annotations, the description is severely incomplete. It does not explain what the analysis output looks like, how the error should be formatted, or what the AI does with it. With several sibling tools present, the lack of contextual detail makes it difficult to select and use this tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one required parameter 'error' with zero description coverage. The tool description does not mention or explain this parameter, its format, or expected content. Since schema coverage is 0% and the description fails to compensate, parameter semantics are entirely undocumented.
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 the tool analyzes a test failure using AI, with a specific verb and resource. However, it does not distinguish itself from sibling tools like classify_failure or analyze_report, which also involve analysis of failures or reports.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. The description gives no context about typical scenarios, prerequisites, or situations where another sibling tool would be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
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 only states that it generates manual test cases, omitting any details about input requirements, output format, limitations, or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
At seven words, the description is extremely concise with no wasted words. However, it is under-specified, lacking the rich detail that would make its brevity effective.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With one parameter, no annotations, and no output schema, the description should carry the load of explaining behavior and return values. It fails to do so, leaving the agent without essential context about what the generated test cases look like.
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 describes the 'requirement' parameter as 'Application requirement or feature', achieving 100% schema coverage. The tool description adds no additional semantic meaning beyond what the schema already provides, so a baseline score of 3 is appropriate.
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 uses the verb 'generate' and the resource 'manual test cases', which clearly distinguishes it from sibling tools like generate_playwright_script and generate_api_tests. However, the phrase 'using AI' is redundant and adds no information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool over its siblings. There is no mention of prerequisites, use cases, or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 only states 'using AI', which hints at a model-based analysis, but does not disclose whether the operation is read-only, what output format to expect, or any error behavior.
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 a single concise sentence with no verbose padding. However, it lacks structural elements like examples or alternative usage, making it efficient but slightly under-specified.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one parameter and no output schema, the description gives minimal context. It doesn't explain what classification output will look like, what categories exist, or how this differs from sibling 'analyze_failure', leaving gaps for effective tool selection.
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 schema fully describes the only parameter 'errorMessage' with a clear description. The tool description adds no further meaning beyond that, so baseline 3 applies.
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 uses a specific verb 'classify' and names the resource 'Playwright and automation failures', which clearly indicates the tool's scope. However, it does not distinguish this from sibling tool 'analyze_failure', which could be seen as overlapping.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus alternatives like analyze_failure, analyze_report, or suggest_fix. The usage context is only implicit (when you have an error message to classify).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the burden of behavioral disclosure. It only says 'using AI', which reveals nothing about the process, output format, or side effects. It fails to explain what 'test scenarios' look like or how they are generated.
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 a single concise sentence with no wasted words. It is efficient, though it could be longer to convey necessary context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is too minimal to be complete in context. It doesn't explain how this differs from sibling generation tools, nor does it describe the output format or expected behavior. The simple schema doesn't compensate for the missing contextual guidance.
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 schema covers 100% of the single parameter (apiDetails), so baseline is 3. The description adds no additional meaning beyond the schema's own description, but it doesn't need to compensate for any coverage gaps.
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 states a clear verb ('Generate') and resource ('API test scenarios'), which is specific to API testing. However, it doesn't explicitly differentiate from sibling tools like generate_test_cases or generate_playwright_script, so it misses the top score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. There is no mention of context, prerequisites, or situations where another sibling tool would be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the core action but gives no information about output format, side effects, limitations, permissions, or determinism. The only added trait is 'using AI', which is vague and not elaborated.
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 one concise sentence, front-loaded with the key verb and resource. Every word adds value and there is no redundancy.
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?
For a simple one-parameter tool with no output schema, the description is adequate but minimal. It does not explicitly mention the output/return value, which would be expected without an output schema. The purpose and input are clear, but the lack of return-value context keeps it from being complete.
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% for the single 'scenario' parameter, which already describes it as 'Application scenario to automate'. The tool description does not add further meaning, so 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 specifies a verb ('Generate'), a resource ('Playwright TypeScript automation scripts'), and a distinctive method ('using AI'). It distinguishes from sibling tools like generate_test_cases and generate_api_tests by narrowing to Playwright TypeScript specifically.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention any exclusions, prerequisites, or context that would help an agent decide between this and sibling generation tools like generate_test_cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure, but it only states that it 'generates' reports. It does not disclose whether the operation is read-only, whether external AI services are invoked, or what the output format might be. This is a significant gap for a generative tool.
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 a single, efficient sentence that is front-loaded with the action. The phrase 'using AI' is somewhat redundant filler, but it does not materially hurt clarity or structure.
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 low complexity (one parameter, no nested objects, no output schema), the description sufficiently conveys the tool's purpose and the input. However, the lack of output format details and sibling differentiation prevents a perfect score.
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 fully covers the single parameter (bugDetails) with a clear description. The tool description adds no extra param semantics, so the baseline 3 is appropriate since the schema already provides sufficient meaning.
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 the tool's function: generating professional bug reports. It uses a specific verb ('Generate') and resource ('bug reports'), but it does not differentiate from sibling tools like analyze_report or classify_failure, which could also relate to bug report generation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention scenarios, prerequisites, or exclusions, leaving the agent to infer usage solely from the tool name and siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It states what it does but does not disclose behavioral traits such as input source, return format, or whether it is a read-only operation. For an analysis tool, this is slightly below adequate.
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 a single, concise sentence that is front-loaded with 'Analyze automation test report' followed by the specific action. Every word serves a purpose with no redundancy.
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?
Given the absence of parameters, annotations, and output schema, the description provides a minimal viable explanation. However, it leaves ambiguity about what 'extract failure details' entails and how the report is provided, making it merely adequate.
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?
The tool has zero parameters, and the baseline for this case is 4. The description adds no parameter details, but none are needed since the schema is empty and coverage is vacuous.
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 the tool's purpose: 'Analyze automation test report and extract failure details' uses a specific verb and resource. It distinguishes from siblings by indicating a report-level analysis, though it doesn't explicitly differentiate from 'analyze_failure'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. The description lacks any mention of prerequisites, exclusions, or recommended use cases relative to sibling tools like 'analyze_failure' or 'classify_failure'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 of behavioral disclosure. It states only that it 'suggests fixes' but does not mention whether it queries an external AI, what output format to expect, or any side effects. Minimal behavioral information is disclosed beyond the basic action.
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 a single, compact sentence that is front-loaded with the core purpose. Every word contributes meaning, and there is no filler or redundancy.
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 is simple (one parameter, no output schema), and the description adequately states its purpose. However, it does not describe the return value or output format, which is a notable gap given there is no output schema to fill that role. The description is minimally viable but not fully complete.
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%, as the errorMessage parameter is already described as 'Playwright or Automation error message'. The description adds no additional meaning about how the parameter should be formatted or used, so 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 a specific action ('Suggest AI fixes') applied to a specific resource ('Playwright and automation failures'). This distinguishes it from sibling tools that analyze, classify, or generate tests, and the verb+resource pattern directly communicates the tool's function without ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like analyze_failure or classify_failure. It lacks any mention of prerequisites, conditions, or scenarios where this tool is preferred, leaving the agent to infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully disclose behavior. It only states that it detects flaky tests using AI, without explaining what inputs it implicitly acts upon, whether it runs tests or analyzes existing results, what output it returns, or any side effects. This is minimal transparency.
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 a single clear sentence with no filler. It is front-loaded with the tool's core purpose and earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of annotations, output schema, and parameters, the description is the sole source of context. It fails to explain what 'detect' means in practical terms (e.g., output format, operational requirements), leaving the agent with significant ambiguity.
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?
The tool has 0 parameters, so the schema already covers all parameters. The description does not introduce any parameter semantics, but none are needed, justifying the baseline score of 4.
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 detects flaky Playwright tests using AI. The verb 'Detect' and resource 'flaky Playwright tests' are specific, and it is distinct from sibling tools like analyze_failure or suggest_fix which handle different tasks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. The description does not mention scenarios, exclusions, or preferred use cases, so the agent receives no direction for selection.
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?
No annotations are provided, so the description carries the burden of behavioral disclosure. It states the action ('check health') but does not describe expected outcomes, return values, or side effects. For a ping operation, this is minimal but lacks detail about what the response will be.
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 a single, short sentence that is front-loaded with the essential information. Every word is purposeful, with no redundancy or filler.
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 simplicity of a ping/health check tool, the description is sufficient to convey the core purpose. However, the absence of an output schema means it could benefit from noting the type of response (e.g., status message), but the current level is adequate for a tool of this triviality.
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?
The tool has zero parameters, and the schema description coverage is 100%. With no parameters to document, the description does not need to explain any, and nothing is missing.
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 the clear verb 'check' and specifies the resource 'QA Copilot MCP server health', making the purpose immediately obvious. It clearly distinguishes itself from sibling tools that analyze failures or generate tests, as this is a standalone health check.
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 for checking server health but provides no explicit guidance on when to use it versus alternatives. Since all sibling tools have clearly different purposes, the context is understandable, but the description does not offer any exclusions or alternative tool references.
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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