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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds minimal context, only that it composes a suite from a list, without describing validation, output details, or any side effects. It is not contradictory, but does not enrich the behavioral picture.

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 redundant information. It efficiently conveys the core function without waste.

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 tool with 4 parameters, 2 of which are optional, and 0% schema coverage, the description is incomplete. It lacks details about the optional parameters, the expected structure of testcases, and any constraints or prerequisites. The existence of an output schema reduces the need to describe return values, but the parameter semantics and usage conditions remain underexplained.

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 partially explains two of the four parameters: 'test case list' maps to testcases, and the enumerated suite types map to target. However, it completely omits sprint and max_duration_minutes. Since the schema has 0% description coverage, the tool description fails to compensate for these undocumented parameters, leaving them ambiguous.

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 a specific verb (creates), the resource (suite), and the source (test case list), and enumerates the six suite types. It is distinct from siblings like testcase_generate or testcase_lint, which focus on generating or validating individual test cases.

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 is given on when to use this tool vs alternatives. The description only states what it does, without mentioning conditions, exclusions, or recommending it for specific scenarios. The distinction from siblings is implicit, not 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 read-only, idempotent, and non-destructive behavior. The description adds no further behavioral context (e.g., side effects, performance, or external dependencies), but it is consistent with the annotations, so this neutral addition warrants a middle score.

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 a single concise sentence, efficiently conveying the core purpose without unnecessary detail. It is well-structured and easy to parse.

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 covers the basic purpose but omits any detail about the parameters, output format, or expected input structure. While an output schema exists, the lack of parameter context makes the tool only partially complete for an agent aiming to invoke 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?

The description gives no explanation of the parameters (modules, testcases, requirements). With 0% schema description coverage, the description fails to compensate for the lack of parameter documentation, leaving the agent without enough context to correctly populate the arguments.

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: generating a requirement and module coverage report for a test case collection. It uses a specific verb ('üretir' – generates) and identifies the resource, making the purpose unambiguous and distinct from siblings.

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 no explicit guidance on when to use this tool instead of alternatives. It does not mention conditions, trade-offs, or comparisons with sibling tools, leaving the agent to infer appropriate usage from the purpose alone.

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 carry the safety profile (readOnlyHint=true, idempotentHint=true, destructiveHint=false), so the description is not required to restate it. The description adds the 'standard' qualifier, implying normalized/consistent output, which is a modest addition beyond the annotations. No contradiction exists — 'generate' aligns with read-only generation.

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 declarative sentence with zero filler. The purpose is front-loaded and every word earns its place. It is efficient without being under-specified to the point of tautology.

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 presence of an output schema covers return-value expectations, so that gap is handled. However, with six parameters and zero schema description coverage, the description leaves four parameters (module, risk_level, include_boundary, include_negative) undocumented. An agent would struggle to configure generation correctly without additional guidance.

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 compensate, but it only implicitly references two of the six parameters (feature and acceptance_criteria). module, risk_level, include_boundary, and include_negative are entirely unexplained in both the schema and description, leaving the agent to guess their meaning and interaction.

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 ('üretir'/'generates'), a specific resource (standard test cases), and the source inputs (feature description + acceptance criteria). This clearly distinguishes it from the sibling tools, which are about linting, normalizing, converting to Xray, composing suites, or reporting coverage rather than generating.

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

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

The phrase 'from feature description and acceptance criteria' implies the generation scenario, but the description never explicitly routes the agent — it does not state when to use this tool versus alternatives or when not to use it. No sibling is named and no exclusion condition is given, leaving selection to inference.

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?

The annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds no extra behavioral context beyond stating it analyzes and returns results, which is consistent with the annotations. It does not contradict them, but it also does not go beyond 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?

The description is a single, clear sentence with no unnecessary words. It directly states the action and the output.

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?

While the output schema covers return details, the description does not provide essential context about the parameters or the distinction from the batch variant. Given the parameter count and lack of descriptions, the description alone is insufficient to use the tool correctly in all scenarios.

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?

There are no parameter descriptions in the schema (coverage 0%), and the description does not explain the purpose or effect of the three parameters (testcase, strict_mode, include_improvement_plan). The names give some hint but not enough for correct usage.

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 ('analiz eder' - analyzes) and identifies the resource (test case) and the outcome (quality score and improvement suggestions). It clearly distinguishes this from sibling tools like testcase_generate or testcase_normalize by its focus on analysis and scoring.

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 no guidance on when to use this tool versus alternatives such as testcase_lint_batch or other sibling tools. There is no mention of single vs batch usage or any decision criteria.

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?

The annotations already cover read-only, non-destructive, and idempotent behavior. The description adds no extra behavioral context such as error handling, side effects, or rate limits. It is consistent with annotations but provides no additional transparency beyond 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?

The description is a single, clear sentence that is appropriately concise for the tool's function. It conveys the essential purpose without unnecessary verbosity, making it easy to parse.

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?

Despite having an output schema, the description omits crucial context about input parameter formats, the behavior of strict_mode, and the nature of the returned statistics. It also does not mention the batch vs. single-use distinction, leaving the tool's context incomplete for an agent.

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 only vaguely acknowledges the 'testcases' parameter through the phrase 'multiple test cases' but does not explain the structure of array items or the meaning of 'strict_mode'. Since the schema has no field descriptions, this lack of detail leaves parameter semantics largely unexplained.

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 that the tool analyzes multiple test cases in batch and returns aggregate statistics, which distinguishes it from the singular testcase_lint sibling. The verb 'analyzes' and the object 'multiple test cases' precisely convey the tool's function.

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 batch analysis of multiple test cases, but it does not explicitly mention when to use this tool instead of alternatives like testcase_lint. The guidance is implied rather than explicit, so it falls short of clearly directing the agent.

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 indicate read-only and idempotent behavior; the description adds no extra behavioral context but is consistent with those hints.

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 a single, concise sentence with no redundant information, front-loading the primary action.

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 provides the core conversion purpose and implies the return of a normalized test case, but lacks parameter explanations; however, the presence of an output schema likely covers the return structure.

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 input schema has no descriptions for either parameter, and the description does not explain what input_data or source_format mean or how the enum values behave, leaving the agent to infer.

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 converts Markdown, Gherkin, JSON, or plain text into the QA-MCP standard, using a specific verb and resource. It is distinct from sibling tools like testcase_to_xray, though it does not explicitly name alternatives.

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 testcase_lint or testcase_to_xray, nor any conditions or 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 establish read-only, non-destructive, idempotent behavior; the description adds a meaningful behavioral detail about using configured tenant values as defaults, but does not elaborate other side effects or output characteristics.

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

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

Two concise sentences with the main purpose front-loaded and no extraneous information. Every sentence 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?

Describes the core conversion and one fallback behavior, but misses enough parameter detail and does not clarify single vs batch usage. With five parameters and a nested object, the description is only barely adequate.

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 parameter descriptions, and only project_key and custom_field_mappings are mentioned (their fallback behavior). The required testcase object, test_type, and include_custom_fields are left unexplained, so schema-description coverage remains well below 50%.

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 clearly that it converts a standard test case into Xray import format. The verb and target format are explicit, and the singular scope is evident from the name and the testcase parameter, distinguishing it from the batch sibling.

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?

Provides limited usage guidance by explaining fallback to tenant-configured values when project_key and custom_field_mappings are omitted, but does not explicitly contrast with sibling tools like testcase_to_xray_batch or testcase_generate, so when-to-use guidance is only implied.

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, idempotentHint=true, and destructiveHint=false, covering safety and idempotency. The description adds the conversion action but does not disclose output details or any side effects beyond what annotations imply. This is adequate given the annotation coverage.

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 zero wasted words. It conveys the core purpose immediately and is appropriately sized.

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 three parameters and zero schema descriptions, the description is severely incomplete. It does not explain the input format, the meaning of test_type or project_key, or any details about the conversion process. The output schema exists, but the agent still lacks essential input guidance.

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%, and the description does not explain any of the three parameters (testcases, test_type, project_key). An agent has no guidance on what these fields mean or how to populate them.

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 action (converts), the resource (multiple test cases), and the target format (Xray). It explicitly says 'multiple' and 'bulk', distinguishing it from the singular sibling testcase_to_xray.

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 implicitly signals it is for batch operations by saying 'Birden fazla test case'i' (multiple test cases). This gives clear context for when to use it, though it does not explicitly name the alternative for single conversions.

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?

The description is consistent with the readOnly and idempotent annotations and indicates a simple retrieval with no side effects, but it adds no behavioral context beyond what the annotations already declare.

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 one short sentence, front-loaded with the action and object, and contains no filler or redundant 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?

For a zero-parameter, read-only retrieval tool with an output schema, the description is sufficiently complete. It does not explain how the mapping template relates to the conversion sibling tools, but that is not required for the simple act of retrieving it.

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

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

The tool has zero parameters, so there is no parameter behavior to document. The input schema fully covers the empty parameter set, and no additional parameter explanation is needed.

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 is a single, specific statement: 'Returns the Xray field mapping template.' It clearly identifies the action and the resource, and it is distinct enough from sibling tools that generate, lint, normalize, or convert test cases.

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 gives no guidance on when to use this tool versus related tools such as testcase_to_xray or testcase_to_xray_batch. An agent has to infer from the name that this template is likely a prerequisite for conversion.

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