mcp

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

Annotations already declare this as destructive and idempotent, so the core behavioral profile is covered. The description adds the 'server-side apply' detail and kubectl analogy, but doesn't disclose additional nuances like conflict resolution or readiness behavior. With annotations present, the bar is lower, and this description meets it without adding much extra 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 two sentences long, front-loaded with the primary purpose, and wastes no words. The kubectl analogy is a useful clarification that earns its place. Perfectly concise for the tool's simplicity.

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 a complete input schema, output schema, and annotations covering destructive/idempotent behavior, the description is sufficient to guide correct invocation. The only missing element is explicit guidance on preconditions or when to avoid this tool, but that's not critical given the schema and sibling context. The overall information is complete enough for the 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?

Schema description coverage is 100%, with every parameter having a meaningful description. The tool description itself does not mention any parameters, so it adds no additional meaning beyond the schema. According to the baseline for high coverage, a score of 3 is appropriate.

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 the exact action (applies), the resource (Kubernetes manifest), and the target (a cluster). It also specifies the mode (server-side apply), which distinguishes it from other k8s tools like create_cluster or patch_k8s_resource. The similarity to `kubectl apply --server-side` reinforces the behavior unambiguously.

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 clearly indicates when to use the tool: whenever a Kubernetes manifest needs to be applied to a cluster. The kubectl analogy provides a familiar reference point. It doesn't explicitly discuss alternatives or exclusions, but the context is clear enough for an agent to select this over sibling tools like delete_k8s_resource or describe_k8s_resource.

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 'Cancels' aligns with the annotations (destructiveHint=true, readOnlyHint=false), but it adds no behavioral context beyond what annotations already declare. It does not disclose consequences, async behavior, or error conditions, so it contributes no additional transparency.

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

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

The description is a single, terse sentence with no filler. It front-loads the core action and resource. While it could benefit from more detail, it is efficiently concise and does not waste words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

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

The tool is simple, and the schema plus annotations cover parameter definitions and safety profile. However, the description lacks any operational context, such as when cancellation is appropriate or what happens after cancellation, leaving some gaps for a new 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?

Schema description coverage is 100%, with both 'name' and 'parent' fully described including required formats. The tool description adds no parameter-level semantics, but the baseline of 3 is appropriate since the schema carries the full meaning.

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 ('Cancels') and the resource ('a specific GKE operation'), which distinguishes it from siblings like get_operation and list_operations. It is specific but lacks additional details about scope or conditions, so it's not 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 guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, such as checking the operation status first, or exclusions for operations that already completed. The description is purely declarative with no usage context.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, which cover the safety profile. The description adds a helpful analogy to `kubectl auth can-i`, reinforcing that this is a non-mutating check. It does not go into deeper behavioral details, but with strong annotations, this is sufficient.

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: two short sentences, with the analogy in the second sentence. It is front-loaded with the main purpose and contains no filler or repetition of schema details.

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, complete schema coverage, and strong annotations, the description is adequately complete. It explains what the tool does and gives a familiar reference. It lacks explicit usage guidance or exclusions, but these are not essential for this read-only check 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 baseline is 3. The description does not add any parameter-specific meaning beyond what the schema already provides; it only gives a general analogy. No extra value is contributed to parameter understanding.

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 checks whether an action is allowed on a Kubernetes resource, using the analogy to `kubectl auth can-i`. This distinguishes it from sibling tools like get/describe/delete/patch, which operate on resources directly.

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 when to use the tool by comparing it to `kubectl auth can-i`, which is a standard command for authorization checks. However, it does not explicitly mention alternatives or when not to use, so it falls short of a 5.

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 are all false, providing no safety signals. The description adds behavioral context about the default Autopilot mode and the need to set autopilot.enabled=false for Standard clusters. It also links to documentation. However, it does not disclose that cluster creation is a long-running operation, potential costs, or required permissions—information an agent would benefit from.

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 concise: three sentences plus a link. It is front-loaded with the core purpose, then provides guidance and a concrete example (gcloud commands). Every sentence contributes useful information 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?

The description covers the main operational choice (Autopilot vs Standard) and references external documentation for deeper config details. It lacks explicit mention of long-running operation behavior or prerequisites, but the link to docs partially compensates. Since an output schema exists, return value details are not required.

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% and both parameters have descriptions. The description adds value by explaining how to configure the cluster parameter, specifically the autopilot.enabled flag, and links to detailed configuration docs. This goes beyond the schema's generic 'cluster resource' 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 clearly states the tool's function: 'Creates a new GKE cluster in a given project and location.' It uses a specific verb (creates) and resource (GKE cluster), and the mention of Autopilot vs Standard mode distinguishes it from sibling tools like create_node_pool and update_cluster.

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 guidance on default behavior (Autopilot) and how to override it for Standard clusters, which is important for selecting the right configuration. It also references gcloud commands for familiarity. However, it does not explicitly contrast with alternative tools like create_node_pool, so it's not a full when/why-not comparison.

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 write operation (readOnlyHint=false) and not idempotent. The description adds no extra behavioral details beyond the verb 'creates'—no mention of async execution, required permissions, or side effects. No contradiction, but no added value.

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 that is immediately clear and contains no filler. It efficiently communicates the core function.

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 fairly straightforward create operation with a complete schema and output schema, the description provides the minimum viable context. It lacks explicit prerequisites (e.g., cluster must exist) or operational behavior (e.g., long-running operation status), but sibling differentiation makes the intended use reasonably clear.

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 both parent and nodePool fully described including format and link. The description itself adds no parameter details, but the schema does all necessary work, so the baseline of 3 applies.

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', the resource 'node pool', and the target 'specific GKE cluster'. This distinguishes it from sibling tools like create_cluster and update_node_pool.

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 the tool is for adding a node pool to an existing cluster but does not explicitly state when to use it over alternatives or any prerequisites. No reference to create_cluster or update_node_pool for contrasting cases.

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 destructiveHint=true, so the description doesn't need to restate destructiveness. However, it adds minimal extra behavioral context beyond the kubectl analogy. It does not mention irreversibility, permission requirements, or cascading effects (though cascade param is in the schema). With annotations covering the safety profile, this is adequate but not enriching.

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 action, and the kubectl analogy is both concise and helpful. No redundant or filler content.

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 a full output schema, detailed parameter descriptions, and destructiveHint annotation, the description provides sufficient context for a delete operation. It could mention that deletion may be asynchronous or that cascade options exist, but these are already in the schema. Overall, it's complete enough for a tool with this complexity.

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 parameters are already well-documented in the input schema. The description adds no additional parameter semantics beyond the generic reference to 'resource'. This matches the baseline for full 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 tool's function with a specific verb ('Deletes') and resource ('Kubernetes resource from a cluster'). It differentiates from siblings like apply_k8s_manifest and patch_k8s_resource by focusing on deletion, and the kubectl delete analogy reinforces intent.

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 versus alternatives, nor any exclusions or prerequisites. The usage context is implied by the name and 'Deletes' verb, but it lacks the direct comparison seen in higher-scoring examples. A clear statement about not using this for cluster-level deletion or mentioning the need for auth would improve it.

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, so the safety profile is known. The description adds behavioral context by comparing to `kubectl describe`, which informs the agent that output will be a detailed, human-oriented summary rather than a raw manifest. It does not contradict annotations, but it could disclose more about return structure or potential errors.

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 and includes the essential analogy to `kubectl describe`. It is front-loaded with the action and resource, with no filler or redundant information. Every word contributes to the tool's understandability.

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 a full input schema, an output schema, and strong annotations, the description is mostly sufficient. It explains the core purpose and behavioral style. However, it lacks explicit differentiation from the closely named `get_k8s_resource` tool, which would make it more complete in the context of the available sibling 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?

All five parameters have descriptions in the input schema (100% coverage), so the schema already explains each parameter. The description itself adds no additional parameter-level detail, which is acceptable given the strong schema coverage. The baseline of 3 applies because the tool description does not need to compensate.

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 ('Shows') and a specific resource ('details of a specific Kubernetes resource'), which clearly states its function. The explicit comparison to `kubectl describe` effectively distinguishes it from sibling tools like `get_k8s_resource` by evoking a well-known kubectl command with distinct behavior.

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 kubectl describe analogy implies when to use this tool (when a user wants a human-readable, detailed view of a resource), but it does not explicitly mention alternatives or state when not to use it. Sibling tools such as `get_k8s_resource` likely serve a similar purpose, and the description lacks direct guidance on choosing between them.

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, destructiveHint=false, so the safe read-only nature is covered. The description adds no extra behavioral context beyond what the annotations and schema already provide, such as returned default fields or behavior when the cluster is not found.

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, grammatically correct sentence that directly states the tool's core function with no filler words. It is front-loaded and minimal.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

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

For a simple GET operation with comprehensive annotations, a high-coverage schema, and an output schema present, the description is sufficient. It could be improved by mentioning the name format or directing users to list_clusters for discovery, but it's not incomplete for the core use case.

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 provides 100% descriptive coverage for both 'name' and 'readMask', including the required format for name. The tool description adds no additional parameter semantics, hence the baseline score of 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 the verb 'Gets' with a specific resource ('details of a specific GKE cluster'), clearly indicating a read operation for a single cluster. It distinguishes from list_clusters by the word 'specific', though it doesn't 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?

The description implies usage when you need details of one cluster, but provides no explicit guidance on when to use alternative sibling tools like list_clusters or get_node_pool. 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 already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds value by comparing output to 'kubectl cluster-info', suggesting the type of information returned (endpoint URLs, server info) without relying on the annotations. No 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 short sentences, front-loaded with the action and resource. The kubectl analogy is a useful, non-redundant addition that improves understandability without bloat.

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 one-parameter schema, an output schema, and comprehensive annotations, the description is fully adequate. The kubectl analogy provides sufficient mental context for the expected behavior and 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?

The input schema has 100% coverage for the single 'parent' parameter, including its format and requirement. The description does not add any additional parameter semantics, so it meets the baseline for well-documented 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 explicitly states 'Gets cluster endpoint information' with a specific verb and resource, and the kubectl cluster-info analogy clarifies the precise scope (endpoint details). This distinguishes it from sibling tools like get_cluster, which likely returns broader cluster metadata.

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 kubectl analogy implies when to use it (to check cluster endpoints), but there is no explicit guidance on when not to use it or how it compares to alternatives such as get_cluster or describe_k8s_resource. It provides clear context but no exclusions or direct 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the kubectl comparison, which offers some behavioral context, but does not disclose additional traits like output formatting, pagination, or error behavior.

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

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

The description is concise, consisting of two sentences with the key purpose stated upfront. There is no superfluous content, and the analogy to 'kubectl logs' is a compact and familiar reference.

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?

Though the description is brief, the rich input schema (100% coverage) and annotations fill in the necessary details, and an output schema exists. The simplicity of the tool (logs) combined with these structured elements makes the description adequate, though it could add a little more context like a mention that output is text.

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 provides 100% coverage with descriptions for all 10 parameters, including name variants, tail, since, and allContainers. The tool description adds no additional parameter semantics beyond what the schema already documents, giving a baseline score.

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 'Gets logs from a Kubernetes container in a pod,' which identifies the specific verb (gets), resource (Kubernetes container/pod), and purpose (retrieving logs). This distinguishes it from sibling tools like get_k8s_resource or describe_k8s_resource, which fetch different data.

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 retrieving logs via the analogy to 'kubectl logs,' but it does not explicitly state when to use this tool versus alternatives (e.g., get_k8s_resource for resource definitions, list_k8s_events for events). There are no exclusions or alternative tool mentions.

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 claims 'Returns the resources in YAML format,' which contradicts the schema's `outputFormat` parameter that defaults to table and supports JSON, YAML, WIDE, and TABLE formats. This is a misleading disclosure of behavior. While annotations indicate read-only and idempotent, the description adds inaccurate information about output behavior, significantly reducing transparency.

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

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

The description is remarkably concise—three sentences with no fluff. It front-loads the core purpose, then adds filtering/output details, and ends with a useful analog. Every sentence serves a clear function, and the structure is 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 tool has 8 parameters and an output schema, the description is mostly adequate, especially with 100% schema coverage. However, the inaccurate YAML statement introduces confusion about the actual output behavior, creating a gap in completeness. The description does mention the primary filters but omits the `customColumns` and `fieldSelector` options, which the schema covers. The misleading output format statement prevents a higher score.

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 each parameter is already well-documented. The description adds a brief mention of filters ('type, name, namespace, and label selectors') that maps to the schema, but does not provide additional semantic detail about parameters like `customColumns` or `fieldSelector`. It neither compensates nor detracts significantly, meriting the baseline score.

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: 'Gets one or more Kubernetes resources from a cluster.' It uses a specific verb ('gets') and identifies the resource type. The comparison to `kubectl get` helps distinguish it from sibling tools like `describe_k8s_resource`, making its purpose unambiguous.

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 the tool—retrieving Kubernetes resources—and references `kubectl get` as a familiar analog. However, it does not explicitly name alternatives or state when not to use it (e.g., for detailed descriptions, use describe_k8s_resource), leaving some ambiguity in edge cases.

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 idempotentHint=true, covering safety and idempotency. The description adds little beyond that; it does not disclose whether the tool waits for rollout completion or returns immediately, which would be useful behavioral context. With annotations carrying the main burden, a score of 3 is appropriate.

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 useful, concise analogy. Every sentence earns its place with no redundancy or filler.

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 a full output schema, comprehensive parameter descriptions, and annotations covering read-only and idempotent behavior, the description is complete for a simple read-only status check. It provides sufficient context for an agent to select and invoke the tool correctly.

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

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

The input schema has 100% description coverage for all four parameters, each with clear descriptions (e.g., resourceType examples). The tool description adds no additional parameter semantics, so the baseline score of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function with a specific verb and resource: 'checks the current rollout status of a Kubernetes resource.' This distinguishes it from sibling tools like describe_k8s_resource or get_k8s_resource, which focus on general resource details. The kubectl analogy further clarifies the exact purpose.

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 kubectl analogy ('similar to running kubectl rollout status') provides clear context for when to use this tool—when you need rollout status specifically. However, it does not explicitly mention alternatives or situations where this tool should not be used, so it falls short of the highest bar for usage guidance.

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

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

Annotations already declare this as a read-only, idempotent, non-destructive operation. The description adds behavioral specificity by naming the client and server versions and the kubectl version analogy, giving the agent a concrete expectation of what data will be returned. 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?

Two short sentences, front-loaded with the action and resource. No superfluous words, every part serves a purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

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

For a simple read-only tool with one well-documented parameter, strong annotations, and an output schema, the description provides sufficient context. It clearly defines the tool's function without needing to explain return values, which are covered by 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?

The input schema covers the single `parent` parameter with a complete format description and 'Required' marker. The description's mention of 'a given cluster' simply restates the parameter's role without adding new details, so no additional parameter semantics are provided beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it retrieves Kubernetes client and server versions for a given cluster, using the specific verb 'Retrieves' and identifying the resource. The analogy to `kubectl version` further clarifies and distinguishes it from sibling tools that retrieve resources, logs, or cluster metadata.

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 establishes clear context: it's for obtaining version information for a given cluster, similar to kubectl version. It doesn't explicitly name alternatives or say when not to use it, but the specificity makes the usage scenario unambiguous.

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 safety profile is transparent. The description adds no extra behavioral nuance beyond confirming it gets details, which aligns 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 a single, front-loaded sentence that exactly defines the tool's purpose without any filler. 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?

For a simple one-parameter read-only tool with an output schema and strong annotations, the description is fully sufficient. No additional context about return values or side effects is needed given the structured metadata.

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 100% coverage for the single parameter 'name', including the full resource format. The description adds no additional parameter meaning beyond what the schema already provides, so baseline 3 is appropriate.

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 the resource 'details of a specific node pool within a GKE cluster', distinguishing it from sibling tools like list_node_pools and create_node_pool. It is specific and unambiguous.

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 context (fetching a single node pool) and the word 'specific' contrasts with list operations, but it does not explicitly name alternatives or exclusion criteria. This is clear context without explicit when-not guidance.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds no additional behavioral context such as error behavior, permissions, or return structure. With annotations present, this is acceptable but not enriched beyond them, warranting a 3.

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?

One concise sentence that states exactly what the tool does. No filler, no repetition of schema details. It earns its place and is front-loaded with the 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?

For a simple read operation with a well-described schema, rich annotations, and an output schema present, the description is sufficient. It doesn't need to explain return values since the output schema covers that. A slightly longer note about GKE-specific context could be useful, but it's not necessary.

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%: the only parameter 'name' is fully described with its format. The description's phrase 'specific GKE operation' adds no new meaning beyond what the schema already states. Baseline 3 applies because 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?

Description is specific and unambiguous: 'Gets the details of a specific GKE operation.' It clearly identifies the verb (gets), resource (GKE operation), and scope (specific), distinguishing it from siblings like 'list_operations' (lists all) and 'cancel_operation' (modifies).

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 tool's use case is immediately clear—retrieve details for one operation. While it doesn't explicitly contrast with alternatives (e.g., 'use list_operations to enumerate'), the context is evident and no exclusions are needed. This matches a 4: clear context, no explicit 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 declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is clear. The description adds the location flexibility ('-' matches all zones and regions), which is useful, but this is also present in the schema description. It does not disclose additional behavioral details like pagination or rate limits, so it adds minimal value beyond 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 crisp sentences: the first states the core action and scope, the second clarifies the location parameter's accepted values. Every word earns its place, with no redundancy or extraneous details.

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 has an output schema, annotations, and 100% parameter schema coverage, the description is adequately complete for a straightforward list operation. It covers the essential purpose and location nuance. It doesn't mention potential caveats like pagination or result limits, but those are likely represented in the output schema. Overall, it is complete enough for an AI agent to invoke 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?

Schema description coverage is 100%, so the schema already documents both parent and readMask parameters thoroughly. The description reiterates the location behavior for parent but does not add new information about the readMask parameter. With full schema coverage, a baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action (Lists), the resource (GKE clusters), and the scope (project and location). It distinguishes from sibling tools like get_cluster (list vs. get) and list_node_pools (clusters vs. node pools). The location flexibility is explicit, making the purpose unambiguous.

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

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 list GKE clusters in a given project and location. It explains the location parameter can be a region, zone, or '-' for all locations, which is key usage guidance. It does not explicitly mention alternatives or exclusions, but the context is sufficient for basic selection.

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, and the description aligns with a read-only enumeration behavior. The description adds the kubectl comparison but no additional behavioral details such as pagination, output size, or error conditions. This is acceptable because annotations cover the safety profile, but the description contributes minimal extra transparency.

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

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

The description is extremely concise, consisting of two short sentences with no redundant information. Every word adds value: it explains the function, the scope (cluster), and an easily understood analogous command.

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?

This is a simple tool with one parameter, a complete input schema, rich annotations, and an output schema. The description sufficiently conveys the tool's purpose and scope without needing to explain return values or additional context, making it complete for the task.

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 single parameter 'parent' is fully described in the schema with its format, and schema description coverage is 100%. The description does not add parameter-level details beyond the schema, which is adequate at the baseline level given the complete schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves available API groups and resources from a Kubernetes cluster, using the specific verb 'Retrieves' and a well-defined resource scope. It also distinguishes itself from sibling tools like list_k8s_events or get_k8s_resource by focusing on API discovery, and the kubectl analogy provides immediate recognition.

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 clear context for when this tool is appropriate: enumerating API groups and resources in a cluster, similar to `kubectl api-resources`. It does not explicitly mention when not to use it or name alternative tools, but the purpose is distinct enough that an agent can infer selection.

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, idempotentHint, and destructiveHint, so the safety profile is established. The description adds the `kubectl events` analogy, which hints at expected behavior, but does not disclose any additional behavioral traits such as pagination, default limits, or permission requirements. It is consistent with annotations, so no 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, front-loaded with the core purpose, and uses zero filler. Every word earns its place, with the kubectl analogy providing meaningful context without bloat.

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 a comprehensive schema, output schema, and clear annotations, the description is nearly complete. The kubectl analogy adds useful context. It lacks explicit guidance on when to use this tool over siblings, but that is partially covered by the purpose clarity and domain context. Adequate for the tool's complexity.

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 100% description coverage for all six parameters, so the schema carries the burden of parameter semantics. The description adds no additional parameter information, leaving the baseline at 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 a specific verb 'Retrieves' with a specific resource 'events from a Kubernetes cluster', clearly distinguishing it from sibling tools like get_k8s_logs or describe_k8s_resource. The analogy to `kubectl events` further reinforces the exact purpose without ambiguity.

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 conveys clear context for when to use the tool (retrieving Kubernetes events) and the kubectl analogy provides a familiar reference. However, it does not explicitly mention when not to use it or name alternative tools, which would be needed for a 5.

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, and idempotentHint=true. The description adds the scoping context of being cluster-specific, which is useful and goes beyond the annotations. It doesn't mention pagination or other behavior, but the output schema fills that gap.

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 that front-loads the action and resource. Every word adds value, with no filler 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?

For a simple read-only list tool with a single parameter, full schema coverage, and an output schema present, the description is complete. It provides enough information for correct selection and invocation.

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 100% coverage with a clear description of the 'parent' parameter format. The tool description adds no additional parameter semantics, so it earns the baseline score of 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 verb 'Lists', the resource 'node pools', and the scope 'for a specific GKE cluster'. This distinguishes it from siblings like list_clusters (lists clusters) and get_node_pool (gets a single pool).

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 the tool is used when you need to list node pools for a given cluster, but it does not explicitly state when to use it versus alternatives or mention any exclusions. Usage context is largely implied by the resource type.

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 safety profile is covered. The description adds location behavior (region, zone, or '-'), but this is already present in the schema description, so no significant new behavioral context is disclosed.

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 no filler. The first sentence states the action and scope, and the second clarifies the location flexibility, making it easy to scan.

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 this is a simple list operation with one well-documented parameter, an output schema, and thorough annotations, the description covers the essential usage. It could mention pagination or default ordering, but the existing context (schema, annotations, and clear purpose) makes it reasonably complete.

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

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

The schema covers 100% of the single parameter 'parent', including its format and the meaning of '-'. The description simply restates this information ('project and location', location can be a region/zone/'-') without adding extra syntax, defaults, or constraints.

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 explicitly states the tool lists GKE operations, using the verb 'Lists' with a clear resource ('operations') and scope ('project and location'). It distinguishes itself from sibling tools like get_operation and cancel_operation by focusing on listing, and from list_clusters/list_node_pools by targeting operations.

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 on when to use it (listing GKE operations) and explains the location parameter's flexibility. It does not explicitly mention alternatives or exclusions, but its purpose is unambiguous enough that an agent can infer when to choose it over related 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 flag destructive behavior, but the description adds no behavioral context beyond the literal action. No mention of permissions, side effects, or result handling, and the kubectl analogy adds limited disclosure.

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 short sentences, no redundancy, and the kubectl analogy is a useful compact reference. Perfectly minimal.

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 schema and annotations cover most safety and parameter details, but the description lacks usage context and alternative recommendations, making it minimally sufficient but not comprehensive for an agent choosing among many Kubernetes 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% for all six parameters with detailed descriptions; the tool description itself adds no parameter semantics beyond the schema, so the baseline score of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: patching a Kubernetes resource, with a helpful kubectl analogy. However, it does not explicitly differentiate from sibling tools like apply_k8s_manifest, relying on the verb 'patch' to convey distinctness.

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?

There is no guidance on when to use this tool versus alternatives; it only mentions similarity to kubectl patch, which is not explicit enough for an agent deciding between patch and apply or delete 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 destructiveHint=true and idempotentHint=true, but the description adds no behavioral context beyond 'updates'. It doesn't disclose what kind of updates are supported, potential side effects, or required permissions. The description simply restates the annotation's implication without additional value.

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, front-loaded with the main verb and resource. It avoids unnecessary words, but is perhaps too terse to be fully informative. Still, it earns its place as a minimal description.

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 a JSON update object) and the presence of annotations and output schema, the description is too minimal. It doesn't explain what can be updated, how the 'update' string should be structured, or any caution about destructiveness. The agent must rely entirely on external references in the 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?

Schema description coverage is 100%, with both parameters having detailed descriptions (e.g., 'name' format and 'update' JSON format). The tool description itself adds no extra parameter meaning, so the baseline of 3 applies because the schema fully carries the 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 'Updates a specific GKE cluster' clearly identifies the verb (updates) and resource (GKE cluster). It distinguishes from sibling tools like create_cluster or delete_k8s_resource, though it doesn't explicitly differentiate from update_node_pool beyond the resource type.

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 (e.g., update_node_pool for node pool changes). It doesn't mention prerequisites, common use cases, or exclusions, leaving the agent without context for tool selection.

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 disclose that the tool is destructive, non-read-only, and idempotent. The description adds no further behavioral context such as side effects, permission requirements, or irreversibility, though it does not contradict the 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 a single, front-loaded sentence with no wasted words. It conveys the core purpose efficiently.

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 low complexity, high schema coverage, and presence of an output schema plus detailed annotations, the description is largely complete. It could benefit from usage pointers, but the essential context is covered by 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%, with both 'name' and 'update' fully described in the input schema. The tool description adds no additional meaning beyond the schema, so a baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Updates') and the resource ('a specific node pool within a GKE cluster'), distinguishing it from sibling tools like update_cluster and create_node_pool. It is specific and immediately understandable.

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 updating a node pool but provides no explicit guidance on when to use this tool versus alternatives such as update_cluster or create_node_pool. No exclusions or alternative recommendations are mentioned.

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