mcp

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

Annotations already provide critical behavioral information: destructiveHint=true, idempotentHint=true, readOnlyHint=false. The description adds some context by specifying 'server-side apply' and the kubectl analogy, which helps understand the apply mechanism. However, it doesn't disclose additional behavioral traits like what 'destructive' means in this context (e.g., overwrites existing resources), authentication requirements, or rate limits.

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 concise with two sentences that directly address the tool's purpose and technical approach. The kubectl analogy is helpful context. No wasted words, though it could be slightly more front-loaded by mentioning key behavioral aspects 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 rich annotations (destructiveHint, idempotentHint), 100% schema coverage, and existence of an output schema, the description provides adequate context. It covers the core purpose and technical mechanism. For a complex Kubernetes operation, it could benefit from more explicit warnings about the destructive nature or guidance on manifest validation, but the structured fields compensate well.

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 all parameters are documented in the schema. The description doesn't add any parameter-specific information beyond what's in the schema. The baseline of 3 is appropriate when the schema does the heavy lifting, though the description could have explained the significance of parameters like forceConflicts or dryRun in the Kubernetes context.

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 ('applies') and resource ('Kubernetes manifest to a cluster') with specific technical detail ('using server-side apply'). It distinguishes from generic apply operations by mentioning the server-side approach, though it doesn't explicitly differentiate from sibling tools like patch_k8s_resource or delete_k8s_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 no guidance on when to use this tool versus alternatives like patch_k8s_resource, delete_k8s_resource, or create_cluster. It mentions similarity to kubectl apply --server-side, but this is technical implementation detail rather than usage guidance for the agent. 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 indicate this is destructive (destructiveHint: true) and idempotent (idempotentHint: true), which the description doesn't repeat. However, it adds valuable context by specifying 'a specific GKE operation,' implying targeting is required. It doesn't contradict annotations, so no deduction is made.

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

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

The description is a single, clear sentence with no wasted words. It's front-loaded with the core action, making it highly efficient and easy to parse.

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

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

Given the destructive nature (annotations cover this), 100% parameter schema coverage, and the presence of an output schema (which handles return values), the description is reasonably complete. However, it lacks usage guidelines, which slightly reduces completeness for a tool that cancels operations.

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 fully documents both parameters (name and parent). The description adds no additional parameter semantics beyond implying specificity, so it meets the baseline of 3 without compensating for gaps.

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 this from sibling tools like 'get_operation' or 'list_operations' beyond the cancel action, which is why it doesn't reach 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 doesn't mention prerequisites (e.g., needing an existing operation), exclusions, or relationships with sibling tools like 'get_operation' for checking operation status before cancellation.

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, non-destructive, idempotent operation with deterministic results (openWorldHint: false). The description adds valuable context by comparing it to `kubectl auth can-i`, which implies it's a permission-checking utility that doesn't modify resources. This enhances understanding beyond what annotations provide, though it doesn't mention rate limits or authentication 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 perfectly concise with just two sentences. The first sentence states the core purpose, and the second provides helpful context with the kubectl analogy. Every word earns its place, and there's no redundancy or unnecessary elaboration.

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 fully cover safety aspects (read-only, non-destructive, idempotent), schema descriptions cover all parameters, and an output schema exists, the description provides exactly what's needed. It clearly explains the tool's purpose and gives practical context with the kubectl comparison, making it complete for this type of authorization-checking 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%, 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, such as explaining relationships between parameters. However, it does provide the high-level context of checking authorization, which aligns with the schema's purpose.

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 whether an action is allowed on a Kubernetes resource.' It specifies the verb ('checks'), resource ('Kubernetes resource'), and action scope ('whether an action is allowed'), which distinguishes it from sibling tools like apply_k8s_manifest or delete_k8s_resource that perform actual operations rather than authorization checks.

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 stating 'This is similar to running `kubectl auth can-i`,' which helps users familiar with Kubernetes CLI understand when to use this tool. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools, such as when actual resource operations are needed instead of permission checks.

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-idempotent, non-destructive operation, but the description adds valuable context beyond this: it specifies the default Autopilot mode (a behavioral trait), references GKE documentation for configuration details, and mentions CLI equivalents ('gcloud container clusters create-auto/create'). It doesn't contradict annotations and provides useful operational 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 appropriately sized (4 sentences) and front-loaded with the core purpose. Each sentence adds value: purpose, documentation reference, default behavior, and CLI analogy. Minor redundancy exists in mentioning Autopilot mode twice, but overall it's efficient with minimal waste.

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

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

Given the tool's complexity (cluster creation), rich annotations (covering safety profile), 100% schema coverage, and presence of an output schema (which handles return values), the description is complete enough. It covers purpose, usage context, behavioral defaults, and external references without needing to repeat 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 already fully documents both parameters (parent format, cluster JSON structure). The description doesn't add specific parameter syntax or format details beyond what the schema provides, though it hints at cluster configuration options via the documentation link. 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 ('Creates a new GKE cluster') and identifies the resource ('in a given project and location'). It distinguishes from sibling tools like 'get_cluster' (read) and 'update_cluster' (modify) by specifying creation rather than retrieval or modification.

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 (creating new clusters) and mentions the default Autopilot mode with an alternative (Standard clusters via autopilot.enabled=false). However, it doesn't explicitly state when NOT to use it (e.g., for updating existing clusters, which would use 'update_cluster') or compare it to other creation-related tools like 'create_node_pool'.

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, non-openWorld operation, which tells the agent it's a standard creation tool with mutation capabilities. The description adds minimal behavioral context beyond this—it doesn't mention potential side effects (e.g., cluster scaling, cost implications), authentication needs, or rate limits. However, it doesn't contradict the annotations, so it meets the baseline for tools 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, direct sentence that efficiently conveys the core purpose without unnecessary words. It's front-loaded with the key action and resource, making it easy for an agent to parse quickly. Every part of the sentence serves a clear 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?

Given the presence of annotations (covering safety and idempotency), a detailed input schema (100% coverage), and an output schema (implied by 'Has output schema: true'), the description provides adequate context for a creation tool. However, it lacks guidance on usage relative to siblings and doesn't hint at behavioral nuances like error conditions or dependencies, leaving minor 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 ('parent' and 'nodePool') well-documented in the schema itself. The description doesn't add any meaningful parameter semantics beyond what's already in the schema (e.g., it doesn't explain JSON structure examples for 'nodePool' or clarify 'parent' format nuances). This aligns with the baseline score when schema coverage is high.

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 ('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 clusters themselves or modifying existing node 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 like 'create_cluster' (for creating entire clusters) or 'update_node_pool' (for modifying existing node pools). It also doesn't mention prerequisites such as needing an existing cluster or specific permissions, leaving the agent to infer usage context from the tool name alone.

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

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

Annotations indicate destructiveHint=true, which the description aligns with by stating 'Deletes'. The description adds context beyond annotations by comparing to kubectl delete, implying it performs a standard Kubernetes deletion operation, but does not detail side effects like cascading deletion (covered in schema) or authentication needs. 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 a single, efficient sentence that front-loads the core action and provides a helpful analogy. There is no wasted text, and it is appropriately sized for a tool with clear annotations and schema.

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 destructive nature (annotations), detailed schema (100% coverage), and presence of an output schema, the description is mostly complete. It could improve by mentioning when to use versus siblings or cautionary notes, but it adequately conveys the tool's purpose and behavior in 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%, so parameters are well-documented in the schema. The description does not add meaning beyond the schema, such as explaining parameter interactions or usage examples. Baseline 3 is appropriate as the schema handles parameter documentation adequately.

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 referencing kubectl delete, which is a precise command-line equivalent. It directly addresses what the tool does 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 provides no guidance on when to use this tool versus alternatives like 'patch_k8s_resource' or 'apply_k8s_manifest' for updates, or 'describe_k8s_resource' for checking before deletion. It mentions similarity to kubectl delete, but this does not clarify usage context or exclusions relative to other 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 provide clear behavioral hints (read-only, non-destructive, idempotent, closed-world). The description adds minimal context beyond this—it implies the tool returns detailed information similar to `kubectl describe`, which suggests richer output than a basic get operation, but doesn't specify format, error conditions, 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 brief and front-loaded with the core purpose in the first sentence. The second sentence adds useful analogy without redundancy. However, it could be slightly more structured by explicitly mentioning key parameters or output characteristics.

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, the description is reasonably complete. It adequately conveys the tool's purpose and analogy, though it lacks usage guidance and deeper behavioral context that could help an agent choose between 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 description coverage is 100%, with all parameters well-documented in the schema. The description adds no additional parameter semantics beyond what the schema provides—it doesn't explain relationships between parameters (e.g., how `name`, `namespace`, and `labelSelector` interact) or provide examples. 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 tool's purpose as 'Shows the details of a specific Kubernetes resource' with a specific verb ('shows') and resource ('Kubernetes resource'), and provides a helpful analogy to `kubectl describe`. However, it doesn't explicitly differentiate from sibling tools like `get_k8s_resource`, which might have overlapping functionality.

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 describe` but doesn't specify when to choose this over siblings like `get_k8s_resource` or `list_k8s_api_resources`, 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 this is a read-only, non-destructive, and idempotent operation, which covers basic safety. The description adds minimal behavioral context beyond this, such as specifying it retrieves 'details' of a 'specific' cluster, but doesn't elaborate on aspects like rate limits, authentication needs, or error handling. With annotations providing core information, the description adds some value but not rich behavioral details.

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 low complexity (a simple read operation), high schema coverage (100%), presence of annotations, and an output schema (implied by 'Has output schema: true'), the description is reasonably complete. It covers the core purpose adequately, though it could benefit from more usage guidance to distinguish it from siblings, which would elevate it to a 5.

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 input schema fully documents both parameters ('name' and 'readMask') with detailed descriptions. The description doesn't add any meaningful semantic information beyond what's in the schema, such as explaining parameter interactions or usage examples. Baseline 3 is appropriate when 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 verb ('Gets') and resource ('details of a specific GKE cluster'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_k8s_cluster_info' or 'describe_k8s_resource', which appear to serve similar purposes, so it doesn't reach the highest 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 like 'get_k8s_cluster_info', 'list_clusters', or 'describe_k8s_resource'. It lacks context about prerequisites, such as needing the cluster name in a specific format, or exclusions for when other tools might be more 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?

The annotations already provide excellent behavioral context (read-only, non-destructive, idempotent, closed-world). The description adds minimal value beyond this by specifying it returns 'endpoint information' and making the kubectl analogy, which helps understand the scope. However, it doesn't add significant behavioral details like rate limits, authentication requirements, or response format beyond what annotations and output schema 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 perfectly concise with just two sentences. The first sentence states the core purpose, and the second provides helpful context through the kubectl analogy. Every word earns its place with zero redundancy or unnecessary elaboration.

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 (single parameter, read-only operation), rich annotations (covering safety and behavior), and existence of an output schema, the description is reasonably complete. It could be slightly improved by explicitly differentiating from sibling tools, but the kubectl analogy provides good contextual understanding for users familiar with Kubernetes tooling.

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 a single well-documented parameter ('parent' with format specification). The description adds no parameter-specific information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even without parameter details in the 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 purpose: 'Gets cluster endpoint information' with a specific verb ('Gets') and resource ('cluster endpoint information'). It distinguishes from some siblings like 'get_cluster' (which might return different metadata) by specifying 'endpoint information', but doesn't explicitly differentiate from all similar tools like 'get_k8s_resource' or 'describe_k8s_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 implied usage guidance through the kubectl analogy ('similar to running `kubectl cluster-info`'), which suggests this is for retrieving high-level cluster connectivity details. However, it doesn't explicitly state when to use this versus alternatives like 'get_cluster' (which might return configuration) or 'describe_k8s_resource' (which might provide detailed resource descriptions).

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, non-destructive read operation. The description adds useful context by comparing to `kubectl logs`, which implies similar behavior and constraints, but doesn't explicitly mention rate limits, authentication needs, or other behavioral traits 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 with just two sentences that directly convey the tool's purpose and context. Every word earns its place, with no redundant information or unnecessary elaboration, making it efficiently front-loaded and easy to parse.

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

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

Given the tool's moderate complexity (10 parameters), rich annotations covering safety and idempotency, and the presence of an output schema, the description is reasonably complete. It clearly states what the tool does and provides helpful context with the `kubectl logs` comparison, though it could benefit from more explicit usage guidance relative to 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?

Schema description coverage is 100%, with all 10 parameters thoroughly 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 of 3 where the schema does the heavy lifting for parameter documentation.

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 logs') and resource ('from a Kubernetes container in a pod'), making the purpose specific and unambiguous. It also distinguishes this tool from siblings by explicitly mentioning its similarity to `kubectl logs`, which helps differentiate it from other Kubernetes-related tools like `describe_k8s_resource` or `list_k8s_events`.

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 by comparing to `kubectl logs`, suggesting this is for retrieving container logs. However, it doesn't explicitly state when to use this tool versus alternatives like `list_k8s_events` for event logs or `describe_k8s_resource` for resource status, nor does it mention any prerequisites or exclusions for usage.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety aspects. The description adds valuable context beyond annotations: it specifies the return format ('YAML format'), mentions it can retrieve 'one or more' resources, and references the `kubectl get` analogy for behavioral expectations. 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 efficiently structured in two sentences: the first states the core purpose and filtering, the second adds format and analogy. Every word earns its place with no redundancy, and key information is front-loaded appropriately.

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 (read-only, idempotent, non-destructive), 100% schema coverage, and presence of an output schema, the description provides sufficient context. It covers purpose, filtering, output format, and a helpful analogy, making it complete for a retrieval tool with strong structured support.

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 well-documented in the schema. The description mentions filtering by 'type, name, namespace, and label selectors', which aligns with schema parameters but doesn't add significant semantic value beyond what's already in the schema descriptions. It also notes the output format, which is covered by the 'outputFormat' parameter.

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'), resource ('Kubernetes resources'), and scope ('from a cluster'), with specific filtering capabilities. It distinguishes from siblings like 'describe_k8s_resource' by focusing on retrieval rather than detailed description, and from 'list_k8s_api_resources' by targeting actual resources rather than API 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 provides clear context by mentioning filtering capabilities and comparing to `kubectl get`, which helps understand when to use it. However, it doesn't explicitly state when to choose this over alternatives like 'describe_k8s_resource' for detailed info or 'list_k8s_api_resources' for API discovery, leaving some sibling differentiation implicit.

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 minimal behavioral context beyond this - the kubectl analogy suggests it might block/wait for completion, but this isn't explicitly stated. No additional behavioral traits like rate limits, authentication needs, or specific error conditions are 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 appropriately concise with just two sentences. The first sentence states the core purpose clearly, and the second provides helpful kubectl context. There's no wasted verbiage or unnecessary elaboration. It could be slightly improved by front-loading more specific information about what 'rollout status' entails.

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 (read-only, idempotent, non-destructive), 100% schema coverage, and the presence of an output schema, the description is reasonably complete for this type of status-checking tool. The kubectl analogy provides useful context. However, it could better explain what specific information the rollout status provides and how it differs from general resource status checks available through 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?

With 100% schema description coverage, all parameters are well-documented in the schema itself. The description doesn't add any meaningful parameter semantics beyond what's already in the schema - it doesn't explain relationships between parameters, provide usage examples, or clarify edge cases. The baseline score of 3 is appropriate when 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: 'Checks the current rollout status of a Kubernetes resource.' It specifies the verb ('checks') and resource ('Kubernetes resource'), and the kubectl analogy provides helpful context. However, it doesn't explicitly differentiate from similar siblings like 'describe_k8s_resource' or 'get_k8s_resource' which might also provide status 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. It mentions similarity to 'kubectl rollout status' which implies usage for monitoring deployment progress, but doesn't specify scenarios where this is preferred over other status-checking tools in the sibling list. No explicit when/when-not instructions or named alternatives 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 read-only, non-destructive, and idempotent behavior, so the description adds minimal value beyond this. It provides context about what is retrieved (client and server versions) and the analogy to `kubectl version`, but does not disclose additional traits like rate limits, authentication needs, or error conditions. 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 front-loaded with the core purpose in the first sentence, and the second sentence provides a helpful analogy without redundancy. It is appropriately sized with no wasted words, making it easy to scan and understand 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 simplicity (one required 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 the tool's role without needing to elaborate on 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 parameter 'parent' fully documented in the schema. The description does not add meaning beyond the schema, such as clarifying the format or usage of the 'parent' parameter. Baseline score of 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 ('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 analogy to `kubectl version` reinforces the 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 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_k8s_cluster_info` or `get_cluster`, nor does it provide exclusions. The context is clear but lacks sibling differentiation.

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, non-destructive, idempotent operation with a closed-world scope, covering key safety traits. The description adds value by specifying it retrieves 'details' (implying comprehensive information) and clarifies the resource scope ('within a GKE cluster'), which helps the agent understand the expected output and context beyond the annotations. No contradictions with annotations are 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?

The description is a single, well-structured sentence that front-loads the core purpose ('Gets the details') and efficiently specifies the resource scope. There is no redundant information or unnecessary elaboration, making it easy for an agent to parse and understand quickly without wasting tokens.

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 required parameter), comprehensive annotations (covering safety and behavior), and the presence of an output schema (which handles return values), the description is complete enough. It clearly states what the tool does, and combined with the structured data, provides sufficient context for an agent to invoke it correctly without needing additional explanation in the description.

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 parameter 'name' fully documented in the schema, including its required format. The description adds minimal semantics by reinforcing it's for 'a specific node pool,' but doesn't provide additional details like examples or edge cases beyond what the schema already specifies. 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 specific action ('Gets') and resource ('details of a specific node pool within a GKE cluster'), making the purpose immediately apparent. It distinguishes this tool from sibling tools like 'list_node_pools' (which lists multiple) and 'get_cluster' (which retrieves cluster-level details), establishing its unique role in fetching individual node pool 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 implies usage by specifying it retrieves details for 'a specific node pool,' suggesting it should be used when the node pool name is known. However, it lacks explicit guidance on when to choose this over alternatives like 'list_node_pools' (for browsing) or 'get_k8s_resource' (for generic Kubernetes resources), leaving the agent to infer context from the tool name and schema alone.

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

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

Annotations already declare this as read-only, non-destructive, and idempotent, so the description adds minimal behavioral context beyond confirming it's a retrieval operation. It doesn't disclose additional traits like error conditions, rate limits, or authentication needs, but doesn't contradict the annotations either.

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 any fluff. It's appropriately front-loaded and every word contributes to understanding the tool's 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?

Given the simple single-parameter schema, comprehensive annotations, and presence of an output schema, the description is reasonably complete for a basic retrieval tool. However, it could better address usage context relative to sibling tools to fully guide 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?

With 100% schema description coverage, the input schema fully documents the single required parameter 'name'. The description adds no additional parameter semantics beyond implying the tool fetches details for a specific operation, which is already clear from the schema's description of the name format.

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 ('details of a specific GKE operation'), making the purpose immediately understandable. It distinguishes this from siblings like 'list_operations' by specifying retrieval of a single operation, though it doesn't explicitly contrast with 'cancel_operation' or other operation-related 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?

No guidance is provided on when to use this tool versus alternatives. While it's implied this is for retrieving details of a known operation, there's no mention of prerequisites (e.g., needing an operation name from 'list_operations') or when to choose this over other operation tools like 'cancel_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 indicate read-only, non-destructive, and idempotent behavior, which the description doesn't contradict. The description adds useful context about location flexibility ('-' for all locations'), but doesn't mention pagination, rate limits, or authentication requirements 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 a single, efficient sentence that immediately states the tool's purpose and key constraint. Every word serves a purpose with no redundancy or unnecessary elaboration.

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 read-only listing tool with comprehensive annotations and an output schema, the description provides exactly what's needed: clear purpose, scope, and location options. The existence of an output schema means return values don't need explanation, making this description complete for its 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?

With 100% schema description coverage, the schema fully documents both parameters. The description mentions location options ('region, zone, or '-' for all locations') which slightly elaborates on the parent parameter, but doesn't add significant meaning beyond the schema's detailed descriptions.

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 ('Lists'), resource ('GKE clusters'), and scope ('in a given project and location'). It distinguishes from siblings like get_cluster (single cluster) and list_k8s_api_resources (different resource type) by specifying its exact domain.

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 location options ('region, zone, or '-' for all locations'), which helps determine when to use this tool. However, it doesn't explicitly mention alternatives like get_cluster for single clusters or when not to use it, keeping it from a perfect score.

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 value by clarifying the tool's similarity to 'kubectl api-resources', which implies it returns a list of resource types without modifying the cluster, aligning with annotations and providing useful operational 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 that are front-loaded with the core purpose and efficiently include a helpful analogy without redundancy. Every sentence adds value, making it appropriately sized and well-structured for quick 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 tool's low complexity (one required parameter), rich annotations covering key behavioral traits, 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 duplicate 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 the single parameter 'parent' fully documented in the schema as the cluster identifier. The description does not add any parameter-specific details beyond what the schema provides, so it meets the baseline for high schema coverage without compensating with extra semantic information.

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 'retrieves' and the resource 'available API groups and resources from a Kubernetes cluster', making the purpose specific and unambiguous. It distinguishes from siblings like 'list_clusters' or 'list_k8s_events' by focusing on API metadata rather than cluster instances or events.

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 it to 'kubectl api-resources', which implies it's for discovering available resource types in the cluster. However, it doesn't explicitly state when to use this tool versus alternatives like 'get_k8s_cluster_info' or 'describe_k8s_resource', leaving some ambiguity in sibling 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 declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false, covering safety and idempotency. The description adds minimal behavioral context by comparing to 'kubectl events', hinting at CLI-like behavior, but doesn't detail rate limits, authentication needs, or response format 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 concise with two sentences that directly state the purpose and provide a useful analogy. It's front-loaded with the core function, though the second sentence could be more informative about usage rather than just a comparison.

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 covering safety and idempotency, and the presence of an output schema, the description is reasonably complete. It adequately explains what the tool does, though it lacks usage guidelines and deeper behavioral insights that could enhance 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 fully documents all 6 parameters. The description adds no additional parameter semantics beyond implying event retrieval, which is already clear from the tool name and purpose. Baseline 3 is appropriate as the schema handles parameter documentation.

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' with a specific verb ('retrieves') and resource ('events'). It distinguishes from siblings by focusing on events rather than resources, logs, or operations, though it doesn't explicitly contrast with similar tools like list_k8s_api_resources.

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 events' but doesn't explain when to prefer this over other sibling tools like get_k8s_logs or list_k8s_api_resources, nor does it specify 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 this is a read-only, non-destructive, idempotent operation with a closed world, so the description adds minimal behavioral context beyond that. It specifies the scope ('for a specific GKE cluster'), which is useful but not rich in behavioral traits like rate limits or auth needs. 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 a single, clear sentence that directly states the tool's purpose without any unnecessary words. It's front-loaded and efficiently conveys the essential information, making it highly concise and well-structured.

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 (one parameter), rich annotations (covering safety and behavior), and the presence of an output schema (which handles return values), the description is reasonably complete. However, it lacks usage guidelines, which slightly reduces completeness in the broader context of 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?

Schema description coverage is 100%, with the schema fully documenting the single required 'parent' parameter. The description adds no additional parameter semantics beyond what's in the schema, such as format 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 ('Lists') and resource ('node pools for a specific GKE cluster'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'list_clusters' or 'get_node_pool', which also involve node pools or listing operations, so it lacks sibling differentiation for 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. For example, it doesn't explain when to choose 'list_node_pools' over 'get_node_pool' (for a single node pool) or 'list_clusters' (for clusters instead of node pools), leaving the agent without context for selection among similar 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 indicate read-only, non-destructive, and idempotent behavior. The description adds useful context beyond this by specifying the location parameter's special value ('-' for all locations') and clarifying the scope ('project and location'), which helps the agent understand operational constraints not covered by 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 clarifying detail about location options. Every word contributes meaning without redundancy, making it efficiently structured and easy to parse.

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

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

Given the tool's low complexity (one parameter), rich annotations (readOnlyHint, idempotentHint, etc.), and the presence of an output schema, the description is complete. It covers the purpose, scope, and key parameter nuance, leaving structured fields to handle the rest, such as safety 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% description coverage, fully documenting the single required 'parent' parameter. The description adds minimal value beyond the schema by reiterating the location flexibility and format, but doesn't provide additional syntax or format details. 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 verb ('Lists') and resource ('GKE operations') with specific scope ('in a given project and location'). It distinguishes from siblings like list_clusters, list_node_pools, and get_operation by specifying it's for operations only, not clusters, node pools, or single 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 for when to use this tool ('Lists GKE operations in a given project and location') and mentions the location flexibility ('region, zone, or '-' for all locations'). However, it doesn't explicitly state when not to use it or name specific alternatives among the many sibling tools, such as get_operation for a single 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?

The description adds valuable context beyond annotations by comparing to 'kubectl patch', which suggests it performs incremental updates rather than full replacements. Annotations already indicate destructiveHint=true (mutation), non-idempotent, and non-readonly, but the description reinforces this by implying modification behavior. 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. The first sentence directly states the purpose, and the second provides helpful context without redundancy. Every word earns its place, 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 destructive nature (annotations show destructiveHint=true), the description adequately conveys the tool's purpose. With 100% schema coverage and an output schema present, the description doesn't need to explain parameters or return values. However, it could better address risk implications or error scenarios for a destructive operation.

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%, providing complete parameter documentation. The description doesn't add any additional parameter semantics beyond what's in the schema (e.g., format details for 'patch' or examples for 'resourceType'). Baseline score of 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 verb ('patches') and resource ('Kubernetes resource'), making the purpose immediately understandable. It distinguishes from siblings like 'apply_k8s_manifest' and 'delete_k8s_resource' by specifying the patch operation. However, it doesn't fully differentiate from 'update_cluster' or 'update_node_pool' which might also modify resources.

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 an analogy to 'kubectl patch', which implies usage context for Kubernetes administrators. However, it doesn't explicitly state when to use this tool versus alternatives like 'apply_k8s_manifest' (for full manifests) or 'update_cluster' (for cluster-level updates). No explicit 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?

The description adds minimal behavioral context beyond what annotations provide. Annotations already indicate this is a destructive, non-read-only, idempotent operation. The description doesn't elaborate on what 'destructive' means in this context (e.g., potential cluster downtime, irreversible changes), nor does it mention authentication requirements, rate limits, or expected response format. However, it doesn't 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 perfectly concise - a single sentence that states exactly what the tool does without any unnecessary words. It's front-loaded with the core functionality and wastes no space on redundant information. This is an excellent example of efficient communication.

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 this is a destructive operation with full parameter documentation in the schema and an output schema available, the description is reasonably complete for its purpose. The main gaps are the lack of usage guidelines and minimal behavioral context beyond annotations, but the presence of comprehensive structured data (annotations, full schema coverage, output schema) reduces the burden on the description.

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 adds no additional parameter semantics beyond what's in the schema - it doesn't explain the relationship between 'name' and 'update' parameters, provide examples of valid update JSON, or clarify edge cases. The baseline score of 3 reflects adequate but minimal value added.

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 GKE cluster'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'update_node_pool' or 'patch_k8s_resource' which also perform updates on Kubernetes resources, missing an opportunity for sibling differentiation.

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. There's no mention of prerequisites (like needing an existing cluster), comparison to similar tools (like 'patch_k8s_resource' or 'update_node_pool'), or specific scenarios where this tool is appropriate versus other update mechanisms.

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 behavioral traits (destructive, idempotent, not read-only), so the description doesn't need to repeat these. It adds minimal context by specifying 'within a GKE cluster', but doesn't elaborate on side effects, permissions, rate limits, or what 'update' entails beyond the schema. 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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy for an agent 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 (destructive update with structured parameters), rich annotations, and the presence of an output schema, the description is reasonably complete. It could improve by addressing usage guidelines or behavioral nuances, but annotations and schema cover safety, parameters, and returns adequately.

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 schema fully documents both parameters ('name' format and 'update' JSON structure). The description adds no additional parameter semantics beyond implying the target is a node pool, which is already clear from the tool name and schema. Baseline 3 is appropriate as 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 action ('Updates') and target ('a specific node pool within a GKE cluster'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'update_cluster' or 'patch_k8s_resource', which would require more specific context about when to use each.

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 like 'update_cluster', 'patch_k8s_resource', or 'create_node_pool'. There's no mention of prerequisites, context, or exclusions, leaving the agent to infer usage from the tool name alone.

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