Google GKE

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

The description adds valuable behavioral context beyond annotations: it specifies 'server-side apply' behavior and compares to kubectl, which helps understand how conflicts are handled. Annotations already indicate destructive (true) and idempotent (true), but the description reinforces this with the apply concept. No contradiction with annotations.

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 perfectly concise with two sentences that each earn their place: the first states the core purpose, the second provides helpful behavioral context with the kubectl analogy. No wasted words, well-structured and front-loaded.

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 (destructive Kubernetes operations), rich annotations (destructiveHint, idempotentHint), 100% schema coverage, and presence of an output schema, the description is complete enough. It covers the key behavioral aspect (server-side apply) without needing to explain parameters or return values that are already documented elsewhere.

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 100%, so the schema fully documents all parameters. The description doesn't add specific parameter details beyond what the schema provides, such as explaining yamlManifest format or parent structure. Baseline 3 is appropriate when schema does the heavy lifting.

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 specific action ('applies a Kubernetes manifest') and resource ('to a cluster'), and distinguishes it from siblings by specifying 'server-side apply' and comparing to kubectl. It differentiates from tools like patch_k8s_resource or delete_k8s_resource by focusing on applying manifests.

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 clear context for when to use this tool ('applies a Kubernetes manifest to a cluster using server-side apply'), but doesn't explicitly state when not to use it or name specific alternatives. It implies usage for manifest application versus other Kubernetes operations, but lacks explicit exclusions or named alternatives.

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 destructiveHint=true and idempotentHint=true, but the description adds value by specifying 'a specific GKE operation,' implying targeting rather than bulk cancellation. It doesn't contradict annotations, and while it doesn't detail side effects like resource cleanup, it provides useful context beyond the structured data.

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, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and earns its place by clearly communicating the core 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?

Given the presence of annotations (covering destructive/idempotent traits) and an output schema (handling return values), the description is adequate for a simple cancellation tool. However, it lacks context on usage scenarios or integration with sibling tools, leaving some gaps in completeness.

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 100%, with both parameters well-documented in the schema. The description doesn't add any additional meaning about the parameters beyond what's already in the schema, so it meets the baseline for high coverage without compensating further.

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 verb ('cancels') and resource ('a specific GKE operation'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_operation' or 'list_operations' beyond the action itself, which prevents a perfect score.

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, prerequisites, or contextual constraints. It doesn't mention sibling tools like 'get_operation' for checking status or specify conditions under which cancellation is appropriate.

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 provide readOnlyHint=true, destructiveHint=false, openWorldHint=false, and idempotentHint=true, covering safety and idempotency. The description adds useful context by mentioning the similarity to `kubectl auth can-i`, which hints at typical Kubernetes authorization behavior, but doesn't disclose additional traits like rate limits, auth needs, or response format beyond what annotations cover.

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 two concise sentences with zero waste: the first states the purpose, and the second provides helpful context by comparing to a known command. It's front-loaded and appropriately sized for the tool's complexity.

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 moderate complexity, rich annotations (covering safety and idempotency), 100% schema coverage, and presence of an output schema (which handles return values), the description is mostly complete. It could be slightly improved by mentioning typical use cases or prerequisites, but it adequately complements the structured data.

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 100%, so the schema fully documents all 5 parameters (verb, parent, resource, namespace, resourceType) with clear descriptions. The description doesn't add any parameter-specific semantics beyond what the schema provides, but with high coverage, the baseline is 3.

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 purpose with specific verb ('checks') and resource ('whether an action is allowed on a Kubernetes resource'), and distinguishes it from siblings by explaining it's similar to `kubectl auth can-i`, which none of the other tools (like apply_k8s_manifest, delete_k8s_resource, etc.) do.

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 clear context by comparing to `kubectl auth can-i`, which implies usage for authorization checks before performing actions. However, it doesn't explicitly state when to use this vs. alternatives like trying the action directly or using other auth tools, nor does it mention 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 indicate this is a non-readOnly, non-destructive operation, which the description aligns with by describing creation. The description adds valuable behavioral context beyond annotations: it explains the default mode (Autopilot), how to override it, and references external documentation for configuration details, though it doesn't cover aspects like rate limits or specific auth requirements.

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 appropriately sized and front-loaded with the core purpose. It uses four sentences efficiently to cover creation, documentation reference, default behavior, and override instructions, with minimal redundancy. The link to external documentation is justified but slightly lengthens the text.

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 (creating a GKE cluster), the description is complete enough: it explains the purpose, provides usage guidance, references documentation for advanced configuration, and notes behavioral defaults. With annotations covering safety aspects and an output schema present (though not detailed here), the description effectively supplements the structured data without needing to explain return values.

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 100%, so the schema already documents both parameters thoroughly. The description doesn't add specific meaning or usage details for the 'parent' or 'cluster' parameters beyond what's in the schema, but it does provide context about cluster configuration options that relate to the 'cluster' parameter's content.

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 specific action ('Creates a new GKE cluster') and resource ('in a given project and location'), distinguishing it from siblings like 'update_cluster' or 'get_cluster' which perform different operations on the same resource type. It provides concrete details about the creation process.

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 clear context about when to use this tool (for creating GKE clusters) and mentions default behavior (Autopilot mode) and how to override it (for Standard clusters). However, it doesn't explicitly state when NOT to use it or mention specific alternatives among the sibling tools, such as 'update_cluster' for modifications.

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 this is a non-readOnly, non-destructive, non-idempotent operation, which the description doesn't contradict. The description adds minimal behavioral context beyond annotations - it implies creation of infrastructure but doesn't elaborate on permissions needed, cost implications, time to provision, or what happens if the operation fails. With annotations covering basic safety, this earns a baseline 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, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately front-loaded with the core action and resource, making it immediately scannable and understandable.

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 that annotations cover the basic operational characteristics (non-readOnly, non-destructive), the schema has 100% description coverage, and there's an output schema (though not provided here), the description is reasonably complete for its purpose. However, for a creation tool in a complex infrastructure context, it could benefit from mentioning typical use cases or pointing to the sibling 'get_node_pool' for verification.

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

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

With 100% schema description coverage, the input schema already fully documents both parameters. The description doesn't add any meaningful parameter semantics beyond what's in the schema - it doesn't explain the relationship between parent and nodePool, provide examples of valid values, or clarify the JSON structure requirements. This meets the baseline for high schema 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 clearly states the action ('Creates') and resource ('a node pool for a specific GKE cluster'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'create_cluster' or 'update_node_pool', which would require mentioning it's specifically for adding node pools to existing clusters rather than creating new clusters or modifying existing pools.

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. It doesn't mention prerequisites (e.g., needing an existing cluster), compare with similar tools like 'create_cluster' or 'update_node_pool', or indicate when this operation is appropriate versus other node pool management approaches.

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 destructiveHint=true, readOnlyHint=false, etc., covering safety and mutability. The description adds context by comparing to `kubectl delete`, which implies command-line-like behavior and potential irreversibility, enhancing transparency beyond annotations without contradiction.

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 two sentences with zero waste: the first states the purpose clearly, and the second adds helpful analogy. It is front-loaded and appropriately sized for the tool's complexity.

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 destructive annotations, 100% schema coverage, and an output schema (implied by context signals), the description is mostly complete. It could improve by mentioning output behavior or error cases, but it adequately covers the core purpose and context for a deletion 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 description coverage is 100%, so the schema fully documents all parameters. The description does not add any parameter-specific details beyond what the schema provides, such as explaining interactions between parameters. Baseline 3 is appropriate given high schema 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 clearly states the specific action ('Deletes') and resource ('Kubernetes resource from a cluster'), and distinguishes it from siblings by not overlapping with other deletion tools (none exist in the sibling list). It provides a helpful analogy to `kubectl delete` for context.

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 deletion tasks in Kubernetes but does not explicitly state when to use this tool versus alternatives like `patch_k8s_resource` for updates or `apply_k8s_manifest` for creation. No exclusions or prerequisites are mentioned, leaving usage context partially inferred.

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 provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false, covering safety and idempotency. The description adds context by comparing to 'kubectl describe', suggesting it returns detailed human-readable output, but doesn't disclose additional behavioral traits like rate limits, authentication needs, or output format specifics beyond what annotations cover.

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 extremely concise with two sentences that are front-loaded and waste-free. The first sentence states the core purpose, and the second adds useful context without redundancy. Every word 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?

Given the tool's moderate complexity (5 parameters), rich annotations (covering safety and idempotency), and the presence of an output schema, the description is reasonably complete. It clarifies the tool's purpose and provides a kubectl analogy, though it could benefit from more explicit differentiation from sibling tools like 'get_k8s_resource'.

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 100%, with all parameters documented in the schema. The description doesn't add any parameter-specific semantics beyond implying 'specific Kubernetes resource' relates to parameters like name, type, and namespace. This meets the baseline of 3 since the schema does the heavy lifting.

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 purpose: 'Shows the details of a specific Kubernetes resource' with a specific verb ('shows') and resource ('Kubernetes resource'). It distinguishes from siblings like 'get_k8s_resource' by emphasizing detailed description rather than basic retrieval, but doesn't explicitly contrast with all similar tools.

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 some guidance by comparing to 'kubectl describe', which implies usage for detailed inspection rather than simple fetching. However, it doesn't explicitly state when to use this versus alternatives like 'get_k8s_resource' or 'list_k8s_api_resources', leaving the distinction somewhat implied rather than 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?

Annotations declare readOnlyHint=true and destructiveHint=false, establishing safety. The description adds no behavioral details beyond the operation name (no failure modes, caching behavior, or API limitations), but does not contradict annotations.

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?

Extremely concise at 7 words. Front-loaded with the verb. However, 'appropriately sized' is debatable given GKE complexity—the description is minimal to the point of underweight, though not redundant.

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 rich annotations (readOnly, idempotent), 100% schema coverage, and existence of an output schema, the minimal description is technically sufficient. However, it provides no context on what 'details' encompasses (config vs runtime) or GKE-specific retrieval behavior.

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

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

With 100% schema description coverage, the structured data fully documents both 'name' (including format pattern) and 'readMask'. The description mentions neither parameter, so it meets the baseline of 3 without adding value.

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 clear verb 'Gets' and resource 'details of a specific GKE cluster'. The word 'specific' implies single-target retrieval, which distinguishes it implicitly from the sibling 'list_clusters', though it doesn't explicitly name the alternative.

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 no guidance on when to use this versus 'list_clusters' or other sibling tools. No prerequisites or conditions are mentioned despite the required 'name' parameter needing a specific resource path format.

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, non-destructive, and idempotent behavior. The description adds useful context by comparing to a known kubectl command, which helps agents understand the scope and output format, though it doesn't detail rate limits or authentication needs.

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 two concise sentences with zero waste. It's front-loaded with the core purpose and efficiently adds context with the kubectl analogy, 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?

Given the simple single parameter, high schema coverage, annotations covering safety, and the presence of an output schema, the description is sufficiently complete. The kubectl analogy provides helpful context, though it could briefly mention what 'endpoint information' entails.

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 100%, so the schema fully documents the single 'parent' parameter. The description doesn't add any parameter-specific information beyond what's in the schema, meeting the baseline for high 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 clearly states the verb 'gets' and resource 'cluster endpoint information', making the purpose understandable. It distinguishes from siblings like 'get_cluster' by specifying it's about endpoint information specifically, though the distinction could be more 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 provides implied usage by comparing to 'kubectl cluster-info', suggesting it's for retrieving cluster metadata. However, it doesn't explicitly state when to use this vs. alternatives like 'get_cluster' or 'describe_k8s_resource', leaving some ambiguity.

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, so the agent knows this is a safe, repeatable read operation. The description adds the kubectl analogy which provides useful context about the tool's behavior, but doesn't disclose additional behavioral traits like rate limits, authentication requirements, or what happens with invalid parameters.

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 extremely concise with just two sentences that efficiently communicate the core purpose and provide a helpful real-world analogy. Every word earns its place, and the information is front-loaded with the primary function stated first.

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 comprehensive annotations (readOnly, idempotent, non-destructive), 100% schema coverage, and the presence of an output schema, the description provides adequate context. The kubectl analogy adds practical understanding, though it could benefit from mentioning typical use cases or limitations.

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

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

With 100% schema description coverage, all 10 parameters are well-documented in the schema itself. The description doesn't add any parameter-specific information beyond what's already in the schema descriptions. The baseline score of 3 reflects adequate coverage through the schema alone.

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 ('Gets logs') and target ('from a Kubernetes container in a pod'), with a helpful analogy to kubectl logs. However, it doesn't explicitly differentiate from sibling tools like list_k8s_events or describe_k8s_resource, which might also provide log-related information.

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. With many sibling tools available (e.g., list_k8s_events, describe_k8s_resource, get_k8s_resource), there's no indication of when this specific log retrieval tool is appropriate versus other diagnostic or monitoring tools.

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, so the agent knows this is a safe, repeatable read operation. The description adds useful context about filtering capabilities and output format (YAML), but doesn't mention rate limits, authentication requirements, or pagination behavior. With annotations covering the safety profile, a 3 is appropriate as the description adds some value but not rich 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.

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

The description is perfectly sized at three sentences, front-loaded with the core purpose, followed by filtering details and output format, ending with a helpful analogy to `kubectl get`. Every sentence earns its place with no wasted words or redundancy.

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 moderate complexity, rich annotations (readOnlyHint, idempotentHint, etc.), 100% schema coverage, and the presence of an output schema, the description is complete enough. It covers the core purpose, filtering options, and output behavior, while structured fields handle parameter details and safety guarantees. No significant gaps remain for agent understanding.

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 100%, so the schema already documents all 8 parameters thoroughly. The description mentions filtering by type, name, namespace, and label selectors (mapping to resourceType, name, namespace, labelSelector parameters) and output format (mapping to outputFormat), but doesn't add significant meaning beyond what the schema provides. The baseline 3 is correct when schema does the heavy lifting.

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 specific action ('Gets one or more Kubernetes resources'), identifies the resource ('Kubernetes resources from a cluster'), and distinguishes it from siblings by specifying it's similar to `kubectl get` (unlike `describe_k8s_resource` or `delete_k8s_resource`). It explicitly mentions filtering capabilities and output format.

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 clear context by mentioning filtering options (type, name, namespace, label selectors) and output format, which helps understand when to use it. However, it doesn't explicitly state when to choose this over alternatives like `describe_k8s_resource` (which might provide more detailed info) or `list_k8s_api_resources` (which lists available resource types).

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, destructiveHint=false, idempotentHint=true, and openWorldHint=false, covering safety and idempotency. The description adds the kubectl analogy, which provides helpful context about the tool's behavior, but doesn't disclose additional traits like rate limits, authentication needs, or specific output characteristics beyond what annotations provide.

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 extremely concise—just two sentences—with zero wasted words. It's front-loaded with the core purpose and efficiently uses the kubectl analogy to provide additional context without verbosity.

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 moderate complexity, rich annotations (covering safety and idempotency), 100% schema coverage, and the presence of an output schema, the description is reasonably complete. It could benefit from more explicit sibling differentiation, but the kubectl analogy and clear purpose make it adequate for the context.

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 100%, with all parameters well-documented in the input schema. The description doesn't add any parameter-specific information beyond what's already in the schema, so it meets the baseline for high schema coverage without compensating value.

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 purpose: 'Checks the current rollout status of a Kubernetes resource.' It specifies the verb ('checks') and resource ('Kubernetes resource'), but doesn't explicitly differentiate from similar siblings like 'describe_k8s_resource' or 'get_k8s_resource' beyond the analogy to kubectl.

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 implied usage context through the kubectl analogy, suggesting it's for monitoring deployment progress. However, it doesn't explicitly state when to use this tool versus alternatives like 'describe_k8s_resource' or 'get_k8s_resource', nor does it mention prerequisites 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 indicate read-only, non-destructive, and idempotent behavior, so the description adds value by specifying that it retrieves both client and server versions, similar to kubectl version, which provides useful context beyond the annotations. No contradictions with annotations.

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 two sentences, front-loaded with the core purpose and followed by a helpful analogy, with no redundant or unnecessary information. Every sentence earns its place by enhancing clarity.

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 (one parameter), rich annotations covering safety and behavior, and the presence of an output schema (which handles return values), the description is complete enough. It effectively communicates what the tool does without needing to explain parameters or outputs in detail.

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 100%, so the schema fully documents the single required parameter 'parent'. The description does not add any additional meaning or details about the parameter beyond what the schema provides, such as examples or edge cases, meeting the baseline for high 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 clearly states the specific action ('Retrieves') and resource ('Kubernetes client and server versions for a given cluster'), and distinguishes it from siblings by focusing on version information rather than cluster creation, resource management, or other operations. The kubectl analogy further clarifies the scope.

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 version information is needed for a cluster, but does not explicitly state when to use this tool versus alternatives like get_cluster or get_k8s_cluster_info, which might provide overlapping or different cluster details. No exclusions or prerequisites are mentioned.

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 cover the safety profile (readOnlyHint, idempotentHint). The description adds contextual scope ('GKE cluster') but does not elaborate on error behavior (e.g., what happens if the node pool doesn't exist) or caching behavior beyond what the name and annotations provide.

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?

Single sentence of 11 words with zero redundancy. Information is front-loaded and appropriately sized for a simple read operation with full schema coverage and rich annotations.

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?

Adequate for a standard getter given the presence of an output schema (covering return values) and complete parameter schema. However, lacks discussion of error conditions (404 scenarios) that would be helpful for an API 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?

With 100% schema description coverage, the baseline is 3. The description adds no parameter-specific semantics (e.g., no mention of the required 'name' parameter or its path format), relying entirely on the schema documentation which is already complete.

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 ('Gets') and resource ('node pool') with clear scope ('within a GKE cluster'). It effectively signals this retrieves a single resource vs. a collection, though it could explicitly distinguish from the 'list_node_pools' sibling tool.

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

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

The phrase 'specific node pool' provides implied usage guidance (use when you have an identifier vs. browsing), but lacks explicit 'when to use this vs. list_node_pools' guidance or prerequisite information like cluster existence requirements.

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?

Consistent with annotations (readOnlyHint=true matches 'Gets'), but adds minimal behavioral context beyond what annotations provide. It omits that GKE operations represent long-running tasks typically polled for status, or that this returns operation metadata/progress.

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?

Extremely efficient at 7 words with no filler. Front-loaded with the verb. However, the brevity borders on under-specification for a tool interacting with async GKE operations, preventing a 5.

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?

Adequate for a simple 1-parameter read tool with 100% schema coverage and output schema present. Missing contextual details about GKE operation lifecycle (polling patterns, status fields) that would help an agent understand return values without inspecting the output schema.

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

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

With 100% schema coverage, the schema fully documents the 'name' parameter format. The description adds no parameter-specific guidance (e.g., where to obtain operation IDs), meeting the baseline score for high-coverage schemas.

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 clear verb ('Gets') and specific resource ('GKE operation'), with 'specific' implying single-item retrieval distinguishing it from sibling 'list_operations'. However, it doesn't explicitly clarify when to prefer this over listing or searching, keeping it from a 5.

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

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

Provides no explicit guidance on when to use this tool versus alternatives like 'list_operations', nor does it mention prerequisites (e.g., needing an operation ID from a prior create/update call). 'Specific' weakly implies usage but offers no concrete 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?

Annotations declare read-only, non-destructive, and idempotent characteristics. The description adds valuable operational context about the location wildcard ('-') behavior that is not fully conveyed by annotations alone, though it could mention pagination or filtering limitations.

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 tightly constructed sentences with zero waste—front-loaded with the action and resource, followed immediately by the critical parameter detail (location wildcard) that affects invocation semantics.

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 presence of output schema and comprehensive annotations, the description appropriately covers the essential invocation context. It successfully conveys the primary filtering mechanism (location scope) but could briefly acknowledge the relationship to 'get_cluster' for single-resource retrieval.

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

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

With 100% schema coverage, the baseline is 3. The description adds meaningful context by highlighting the location wildcard behavior ('-' for all locations), which clarifies the 'parent' parameter usage beyond the schema's formal description.

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 ('Lists') and resource ('GKE clusters') with clear scope ('in a given project and location'), effectively distinguishing it from sibling 'get_cluster' (singular retrieval) and 'list_node_pools' (different resource).

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 implicit usage guidance by explaining location parameter flexibility (region, zone, or '-' for all locations), but lacks explicit guidance on when to choose this over 'get_cluster' or other list operations.

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, destructiveHint=false, openWorldHint=false, and idempotentHint=true, covering safety and idempotency. The description adds valuable context by comparing it to kubectl api-resources, which implies it returns a structured list of resources and groups, and suggests it's for cluster metadata discovery rather than resource data. No contradictions with annotations.

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 two concise sentences with zero waste: the first states the purpose clearly, and the second provides a helpful analogy. Every sentence earns its place by adding clarity and context without redundancy.

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 low complexity (single parameter, read-only), rich annotations covering safety and behavior, and the presence of an output schema (which handles return values), the description is complete enough. It effectively communicates the tool's purpose and usage context without needing to explain parameters or outputs.

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 100%, with the schema fully documenting the single required 'parent' parameter. The description doesn't add any parameter-specific details beyond what's in the schema, so it meets the baseline of 3 for high schema coverage without extra value.

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 specific action ('Retrieves') and resource ('available API groups and resources from a Kubernetes cluster'), and distinguishes it from siblings by specifying it's about API resources rather than clusters, nodes, operations, or other resources. The kubectl analogy further clarifies the scope.

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 clear context for when to use this tool (to get API groups/resources), but doesn't explicitly state when not to use it or name alternatives among siblings. The kubectl analogy implies it's for discovery/listing purposes, but no explicit exclusions or comparisons to tools like list_clusters or list_k8s_events are provided.

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 this is a read-only, idempotent, non-destructive operation, so the description doesn't need to repeat safety aspects. It adds value by specifying the kubectl analogy, which implies it returns event listings similar to that command, but doesn't detail behavioral traits like rate limits, authentication needs, or output format. No contradiction with annotations.

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 highly concise and front-loaded, consisting of two clear sentences that directly state the tool's function and provide a useful analogy. There is no wasted text, repetition, or unnecessary elaboration, making it efficient for an agent 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?

Given the tool's moderate complexity (6 parameters), rich annotations (read-only, idempotent), and the presence of an output schema, the description is reasonably complete. It covers the core purpose and context via the kubectl analogy, though it could benefit from more explicit usage guidelines. The output schema likely handles return values, reducing the need for description details.

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 100%, with all parameters well-documented in the input schema (e.g., 'parent' format, 'limit' default). The description adds no additional parameter semantics beyond the schema, such as explaining interactions between parameters like 'namespace' and 'allNamespaces.' Baseline 3 is appropriate since the schema handles the heavy lifting.

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 purpose: 'Retrieves events from a Kubernetes cluster.' It specifies the verb ('retrieves') and resource ('events from a Kubernetes cluster'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'describe_k8s_resource' or 'get_k8s_logs' that might also retrieve information, though the kubectl analogy helps.

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 some implied usage context by comparing it to 'kubectl events,' which suggests it's for monitoring or debugging events in a cluster. However, it lacks explicit guidance on when to use this tool versus alternatives like 'describe_k8s_resource' for detailed resource info or 'get_k8s_logs' for logs. No exclusions or prerequisites are mentioned.

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 fully declare read-only, idempotent, non-destructive behavior. The description aligns with these (using 'Lists') but adds minimal additional behavioral context beyond what annotations provide (e.g., no mention of pagination, empty result handling, or API rate limits). No contradictions present.

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?

Single efficient sentence with no redundancy. The plural 'Lists' and phrase 'for a specific' front-loads the scoping appropriately. Every word 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?

Given the simple single-parameter input schema with full annotation coverage and presence of an output schema, the description is minimally sufficient. However, it lacks contextual guidance regarding sibling relationships and operational prerequisites that would make it complete for an agent navigating multiple similar tools.

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

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

Schema coverage is 100% with the 'parent' parameter fully documented in the schema including format examples. The description implies the 'specific GKE cluster' requirement but does not elaborate on parameter syntax or semantics beyond what the schema already provides, warranting baseline score 3.

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 specific verb 'Lists' and resource 'node pools' and contextualizes it with 'GKE cluster', making the purpose clear. However, it does not explicitly distinguish from sibling tools like get_node_pool (singular retrieval) or list_clusters (different resource level), which would earn a 5.

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

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

No explicit when-to-use or when-not-to-use guidance is provided. The description does not mention prerequisites (e.g., requiring the cluster to exist first) nor does it suggest alternatives like using get_node_pool for single pool details.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds valuable behavioral context about the wildcard location matching ('-' for all zones/regions), but lacks details on pagination behavior or operation status filtering.

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 efficient sentences with zero waste. Main purpose front-loaded, second sentence provides specific parameter guidance. No redundancy with schema or annotations.

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?

Adequate for a single-parameter list operation with output schema present. Covers resource type (GKE), scope, and wildcard filtering. Could note pagination or that results include long-running operation statuses, but output schema mitigates need to describe return values.

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

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

Schema coverage is 100%, establishing baseline 3. Description adds semantic clarity by explicitly stating location can be 'a region, zone, or '-'', reinforcing the domain values for the parent parameter beyond the schema's format specification.

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 specific verb (Lists), resource (GKE operations), and scope (project/location). Implicitly distinguishes from sibling get_operation by describing location-based filtering, but does not explicitly contrast when to use listing vs. single retrieval.

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 clear usage guidance for the location parameter (using '-' for all locations), but lacks explicit when-to-use/when-not-to-use guidance regarding alternatives like get_operation.

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 destructiveHint=true, readOnlyHint=false, idempotentHint=false, and openWorldHint=false, covering safety and idempotency. The description adds minimal behavioral context by comparing to 'kubectl patch', hinting at partial updates, but doesn't elaborate on side effects, permissions needed, or rate limits. No contradiction with annotations exists.

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 extremely concise with just two sentences, front-loading the core purpose. Every word earns its place, though it could be more informative. No redundant information or fluff is present, 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.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (destructive mutation with 5 parameters) and rich annotations/output schema, the description is minimally adequate. It states the purpose but lacks context on usage, error handling, or behavioral nuances. With annotations covering safety and an output schema likely detailing results, it meets a basic threshold but leaves gaps in practical 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 100%, with all parameters well-documented in the input schema. The description adds no additional parameter semantics beyond the schema, such as format examples for 'patch' or constraints on 'resourceType'. Baseline score of 3 is appropriate since the schema carries the full burden.

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 ('patches') and resource ('Kubernetes resource'), providing a specific verb+resource combination. It distinguishes from siblings like 'delete_k8s_resource' (destructive deletion) and 'apply_k8s_manifest' (applying full manifests). However, it doesn't explicitly differentiate from 'update_cluster' or 'update_node_pool' which might also modify Kubernetes resources, keeping it from a perfect score.

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. It mentions similarity to 'kubectl patch' but doesn't specify when patch is preferred over 'apply_k8s_manifest' (for full resource definitions) or 'update_cluster' (for cluster-level updates). No context about prerequisites, error conditions, or typical use cases is provided.

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 cover key traits: readOnlyHint=false, destructiveHint=true, and idempotentHint=true. The description adds no behavioral details beyond this, such as rate limits, authentication needs, or what specific aspects of the cluster get updated. It doesn't contradict annotations, but offers minimal additional context.

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, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and wastes no space, making it easy to parse quickly.

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 (updating a GKE cluster with destructive potential), annotations provide safety and idempotency info, and an output schema exists. However, the description lacks critical context like update scope, side effects, or error handling, making it minimally adequate but incomplete for informed 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 100%, with clear documentation for both parameters. The description adds no meaning beyond the schema, such as examples or constraints on the 'update' JSON format. Baseline 3 is appropriate since the schema fully handles parameter semantics.

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 ('Updates') and resource ('a specific GKE cluster'), which is specific and unambiguous. However, it doesn't differentiate from sibling tools like 'update_node_pool' or 'patch_k8s_resource' that also perform updates on related Kubernetes resources, missing explicit distinction.

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. The description lacks context about prerequisites, such as needing an existing cluster, or comparisons to siblings like 'patch_k8s_resource' for partial updates or 'create_cluster' for new clusters, leaving usage unclear.

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 cover key traits (destructive, idempotent, not read-only, not open-world), but the description adds value by specifying the resource scope ('within a GKE cluster') and implying a targeted update. It doesn't contradict annotations and provides useful context beyond them, though it could mention potential impacts like downtime or permissions.

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, front-loaded sentence that efficiently conveys the core purpose without unnecessary words. It's appropriately sized for the tool's complexity, with every part contributing directly to understanding.

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 presence of annotations (covering safety and behavior), a detailed input schema, and an output schema (implied by 'Has output schema: true'), the description is reasonably complete. It could improve by addressing usage guidelines or sibling differentiation, but it provides adequate context for a mutation tool with good structured data.

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 100%, with clear documentation for both parameters ('name' format and 'update' JSON structure). The description doesn't add semantic details beyond the schema, such as examples or constraints, so it meets the baseline for high schema coverage without extra value.

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 ('Updates') and target resource ('a specific node pool within a GKE cluster'), which is specific and unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'update_cluster' or 'patch_k8s_resource' beyond the resource type, missing full sibling distinction.

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 'update_cluster', 'create_node_pool', or 'patch_k8s_resource'. It lacks context about prerequisites, use cases, or exclusions, leaving the agent to infer usage from tool names alone.

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