Server Quality Checklist

Tool Scores

• exec_in_podA
  3.8/5.0
  Behavior3/5

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

  With no annotations provided, the description carries the full burden of behavioral disclosure. It does disclose the core behavior and that output is combined, and it correctly situates the tool relative to spawned debug containers. However, it does not mention potential side effects of running arbitrary commands, timeout/exit-code behavior, or prerequisites like the pod/container needing to be running.

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

  Conciseness5/5

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

  The description is two sentences with no wasted words. The core operation is front-loaded, and the second sentence adds valuable context about the companion spawn tool rather than repeating obvious details.

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

  Completeness3/5

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

  An output schema exists, so return-value details are not the description's responsibility. Still, the tool has a moderate parameter count, no annotations, and relevant siblings such as get_pod_logs and spawn_ephemeral_debug_container; the description provides the key intended use but leaves out prerequisites, timeout semantics, and explicit alternative routing.

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

  Parameters2/5

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

  Schema description coverage is 0%, so the description must compensate. It partially explains pod, command, and container selection through natural language, but it does not clarify timeout_seconds, namespace defaults, or what happens when container is left empty. With five parameters and no schema descriptions, this is a meaningful gap.

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

  Purpose5/5

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

  The description clearly states the verb and resource: run a shell command inside a pod and return its combined output. It also distinguishes itself from the sibling spawn_ephemeral_debug_container by explaining that this tool is how you use a debug container once it is running.

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

  Usage Guidelines4/5

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

  The description gives clear usage context: it is meant for executing commands in a running container, particularly ephemeral debug containers created by spawn_ephemeral_debug_container. It does not explicitly state when not to use it or compare against alternatives like get_pod_logs, so it stops short of full when/when-not guidance.

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

• describe_resourceA
  4/5.0
  Behavior3/5

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

  With no annotations, the description carries the behavioral disclosure burden. It accurately states the tool runs kubectl describe and returns raw text with the event tail, which implies a read-only operation. It does not explicitly state whether it is non-mutating, what permissions are needed, or that output can be verbose.

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

  Conciseness5/5

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

  The description is a single well-structured sentence that front-loads the command and resource, then explains the output format and concrete use cases. There is no filler or redundant repetition of the tool name or schema.

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

  Completeness4/5

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

  For a simple read-only describe tool with an output schema present, the description covers the command, resource scope, output shape, and typical use cases. It could be slightly more complete with an explicit read-only or permission note, but the core information needed to invoke it correctly is present.

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

  Parameters3/5

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

  Schema description coverage is 0%, and while the parameter names are self-explanatory, the description only partially compensates by listing example kind values ('pod, deployment, replicaset, service') and implying any resource can be used. The name and namespace parameters are left to inference from their names and the schema default.

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

  Purpose5/5

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

  The description uses a specific verb ('Run kubectl describe'), a clear resource target ('any resource'), and explicitly states the return value ('raw text, including its event tail'). It also differentiates itself from get_events by mentioning failure modes that get_events cannot capture.

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

  Usage Guidelines4/5

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

  It provides clear usage context by naming scheduling failures, probe failures, and admission errors, and explicitly references get_events as an alternative with a limitation. It does not, however, exhaustively list when not to use the tool or compare with all other siblings.

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

• list_argocd_appsA
  4.1/5.0
  Behavior3/5

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

  With no annotations, the description carries the behavioral disclosure burden. It does reveal that the tool returns health and sync status and is a discovery step, but it does not explicitly state that it is read-only, mention scope limitations, or describe project-filtering behavior. This is adequate but leaves some gaps.

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

  Conciseness5/5

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

  Two tight sentences front-load the main action and immediately provide the practical workflow for discovering an application name. No unnecessary words or filler.

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

  Completeness4/5

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

  For a low-complexity list tool with an output schema, the description covers the essential discovery workflow and sibling routing. The main missing piece is documentation of the optional project parameter, which is a minor but real gap.

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

  Parameters2/5

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

  Schema description coverage is 0%, and the description does not explain the optional 'project' parameter, what it filters, or how the default empty string behaves. The parameter name is self-descriptive to a degree, but the description adds no semantic value for correct invocation.

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

  Purpose5/5

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

  The description uses a specific verb ('List'), a concrete resource ('ArgoCD applications'), and the key output ('health and sync status'). It also names the sibling tools that consume its result, distinguishing it clearly from get_argocd_app_status and get_live_diff.

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

  Usage Guidelines5/5

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

  It explicitly tells the agent to use this tool first to discover an application name when unknown, and names the alternatives to use once the name is known. This provides clear when-to-use and when-not-to-use guidance.

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

• find_dynatrace_entityA
  4.2/5.0
  Behavior3/5

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

  No annotations are provided, so the description carries the burden of behavioral disclosure. It does reveal partial-name matching and optional type filtering, and the verb 'Look up' implies a read-only operation. However, it does not describe return cardinality, multiple-match behavior, or failure modes, which are relevant for a partial-match lookup.

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

  Conciseness5/5

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

  The description is two sentences with the operation and options front-loaded, followed by the use-case context. There is no redundant wording or filler, and every sentence contributes to understanding.

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

  Completeness4/5

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

  For a simple two-parameter lookup, the description covers the purpose, input semantics, and the connection to `correlate_trace`. An output schema exists, so return-value documentation is already handled. The only minor gap is explicit workflow guidance tying it to siblings like `list_pods`, but the description is still sufficiently complete.

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

  Parameters4/5

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

  Schema description coverage is 0%, so the description must compensate. It explains that `name` is a partial display name and that `entity_type` is an optional filter, providing concrete examples like 'CLOUD_APPLICATION' and 'KUBERNETES_CLUSTER'. This adds meaning beyond the bare schema, though it omits minor details like case sensitivity or default filter behavior.

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

  Purpose5/5

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

  The description states a specific verb ('Look up'), resource ('Dynatrace entity IDs'), and matching behavior ('by (partial) display name'). It also names the downstream consumer ('correlate_trace needs'), which clearly distinguishes it from the Kubernetes/ArgoCD sibling tools.

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

  Usage Guidelines4/5

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

  The description explicitly says 'Use this to bridge a Kubernetes pod/workload name to the Dynatrace entity ID that correlate_trace needs,' giving a clear context for when this tool is appropriate. It does not mention exclusions or alternative lookup methods, but the stated use case is sufficient for an agent to route correctly.

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

• get_argocd_app_statusA
  4.2/5.0
  Behavior4/5

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

  With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses what the operation returns: health, sync status, last sync result, error conditions, and unhealthy/out-of-sync managed resources. The read-only nature is implied by 'Get', and the description avoids hiding side effects. It could add permission or error behavior details, but for a status read this is solid.

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

  Conciseness5/5

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

  The description is two sentences with no filler. The first sentence front-loads the tool's purpose and outputs, and the second provides valuable workflow context. Every word earns its place.

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

  Completeness4/5

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

  For a single-parameter status read tool with an output schema, the description covers the essential behavior and even suggests the next steps. It is complete enough for correct invocation, though it could mention how to obtain the app name (e.g., from list_argocd_apps) or note behavior when the app does not exist. Minor gap only.

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

  Parameters3/5

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

  Schema description coverage is 0%, and the description does not explicitly define the 'app' parameter beyond referring to 'an ArgoCD application.' The parameter name and type are self-explanatory enough for a single required argument, and the description implies 'app' is the ArgoCD application identifier, but it does not fully compensate for the lack of schema documentation. This is adequate but not rich.

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

  Purpose5/5

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

  The description uses a specific verb ('Get') and identifies the exact resource (an ArgoCD application's status), then enumerates the specific status categories covered: health, sync status, last sync operation result, error conditions, and unhealthy/out-of-sync resources. This clearly distinguishes it from the sibling tools like get_live_diff and list_pods.

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

  Usage Guidelines4/5

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

  The description explicitly positions this tool as a 'good starting point before drilling into get_live_diff or the underlying pods,' which gives the agent useful workflow context. It does not explicitly state when not to use it, but the recommended sequencing relative to alternatives is clear.

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

• get_eventsA
  4.2/5.0
  Behavior4/5

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

  With no annotations, the description carries the burden and discloses meaningful behavior: chronological ordering, substring-match filtering, and cursor-based pagination. It does not mention namespace/limit semantics, but these are minor for a read-only paging tool with an output schema.

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

  Conciseness5/5

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

  Three sentences, each with a distinct purpose: ordering, filtering, and pagination use case. No filler and the most important distinction is front-loaded.

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

  Completeness4/5

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

  Given an output schema, the description sufficiently covers ordering, filtering, pagination, and a motivating scenario. The only notable gap is the behavior of the namespace parameter (default '' likely cluster-wide), but this does not block correct invocation for the common case.

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

  Parameters3/5

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

  Schema coverage is 0%, and the description compensates for two parameters: involved_object (substring match) and cursor (continue from previous next_cursor). It adds no meaning for limit or namespace, so it only partially compensates for the missing schema descriptions.

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

  Purpose5/5

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

  States a specific action ('Page through Kubernetes events') and resource, with clear scope (chronological order, oldest first). The Kubernetes events resource is distinct from all sibling tools, so an agent can select it without ambiguity.

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

  Usage Guidelines4/5

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

  Gives clear usage context: filter by involved_object, continue pagination with cursor, and the incident-timeline scenario. It does not explicitly name alternatives or exclusions, but the event resource is unique among siblings and the incident use case clarifies when it is relevant.

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

• get_pod_logsA
  4.2/5.0
  Behavior4/5

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

  With no annotations provided, the description carries the full disclosure burden. It clearly conveys a read-only fetch behavior and adds the important nuance that previous=True retrieves logs from the last crashed instance. It does not discuss permissions or edge cases, but the core behavioral trait is well disclosed.

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

  Conciseness5/5

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

  Two purposeful sentences, front-loaded with the main action and then providing the most important parameter guidance. No filler or repetition.

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

  Completeness4/5

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

  Given that an output schema exists and the tool has a clear read-only purpose, the description is largely complete. It covers the primary use case, the optional container, and the crucial previous behavior. It could be more explicit about scope alternatives, but nothing essential is missing for basic correct invocation.

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

  Parameters3/5

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

  Schema description coverage is 0%, so the description must compensate. It clarifies the container and previous parameters, and 'recent' loosely maps to tail_lines. However, it does not add detail about namespace or tail_lines semantics beyond what the schema titles already imply.

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

  Purpose5/5

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

  The description opens with a specific verb and resource: 'Fetch recent logs for a pod (optionally a specific container).' This clearly distinguishes the tool from siblings like list_pods, describe_resource, and exec_in_pod, and it immediately communicates the tool's core operation.

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

  Usage Guidelines4/5

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

  The description gives a concrete usage context: 'usually what you want after a CrashLoopBackOff.' It also explains when to set previous=True. It does not explicitly name alternatives or when not to use this tool, but the usage context is clear enough to guide an agent.

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

• get_live_diffA
  4.5/5.0
  Behavior4/5

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

  No annotations are provided, so the description carries the full transparency burden. It discloses that the tool reports drift as added/removed/changed spec fields and intentionally ignores resourceVersion, managedFields, and status subresource noise. This gives meaningful behavioral insight beyond the schema and implies a read-only comparison operation.

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

  Conciseness5/5

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

  Three sentences, all information-dense and front-loaded. The first sentence states the primary function, the second clarifies the exact nature of the output, and the third anchors the use case. No filler or redundant restatement of the tool name.

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

  Completeness5/5

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

  The tool is simple (one required parameter), has an output schema, and the description covers what it does, what it reports, what it ignores, and when to use it. Nothing necessary for correct invocation or selection is missing.

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

  Parameters4/5

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

  The schema has only an 'app' string with 0% description coverage. The tool description compensates by repeatedly framing the operation as comparing an ArgoCD application, making it clear that 'app' refers to the target ArgoCD application name. It does not belabor syntax, but with a single obvious parameter this is sufficient.

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

  Purpose5/5

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

  The description states a specific verb ('Compare') and resource ('an ArgoCD application's desired (git) state against what is actually running'), and precisely defines the output as drift in spec fields while ignoring metadata noise. This clearly distinguishes it from sibling tools like get_argocd_app_status or describe_resource, so an agent knows exactly what this tool is for.

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

  Usage Guidelines4/5

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

  The description gives explicit use-case guidance: 'This is the tool for' detecting out-of-band kubectl edits or checking if a live pod spec matches git. It does not name specific alternative tools or when-not-to-use conditions, but the intended context is clear enough for an agent to select it appropriately.

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

• list_podsA
  4.5/5.0
  Behavior4/5

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

  With no annotations, the description carries the full transparency burden and does a good job: it discloses that the tool surfaces phase, restart counts, and container status reasons, and explains the label-matching behavior for the app parameter. It does not mention authorization or pagination, but these are less critical for a read-only list operation.

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

  Conciseness5/5

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

  The entire description is one dense, front-loaded sentence with no filler. It leads with the action, then lists scoping options in a compact parenthetical, then states the useful output. Every clause adds information.

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

  Completeness4/5

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

  The description covers the key invocation details and the important output semantics, and an output schema exists to fill in return type details. A minor gap is the default scope when no namespace is provided (all namespaces vs. current namespace), but this is not enough to reduce completeness further.

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

  Parameters5/5

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

  Schema description coverage is 0%, yet the description manually explains all three parameters: namespace, a raw label selector, and an ArgoCD app name matched via the 'app.kubernetes.io/instance' label. This adds meaning the bare schema does not, so an agent knows exactly what values to supply and how filtering works.

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

  Purpose5/5

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

  The description starts with the specific verb-resource pair 'List pods' and then details the optional scoping dimensions (namespace, label selector, ArgoCD app name), making the purpose unmistakable. It differentiates itself from siblings like get_pod_logs or describe_resource by focusing on the pod inventory/health overview. The mention of App tracking label also clarifies exactly what 'app' means.

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

  Usage Guidelines4/5

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

  The description clearly implies when to use the tool: to view pods and quickly detect unhealthy containers, with filters that can narrow scope. It stops short of explicitly contrasting with alternatives or stating when not to use it, but the scoping options and health-surfacing goal provide a clear usage context.

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

• correlate_traceA
  4.7/5.0
  Behavior4/5

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

  With no annotations, the description itself carries the disclosure burden. It clearly signals a non-mutating reporting operation ('report', 'detected against'), names the output payload, and explains the Dynatrace relative timeframe syntax. It doesn't explicitly state read-only/permission assumptions, hence not a 5.

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

  Conciseness5/5

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

  Two dense sentences with no filler; the core behavior and timeframe syntax are front-loaded, and the use-case statement is a valuable final anchor.

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

  Completeness5/5

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

  For a 2-parameter tool with an output schema, the description covers the entity-ID prerequisite, the timeframe dialect, and the expected result categories. No additional context is necessary for an agent to invoke it correctly.

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

  Parameters4/5

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

  Schema coverage is 0%, so the description compensates: dynatrace_entity_id is sourced via find_dynatrace_entity, and timeframe is documented with Dynatrace relative syntax and examples. It stops short of noting the default value or allowable units, but the schema provides the default.

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

  Purpose5/5

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

  States a specific operation ('report') on a Dynatrace entity and defines the output: identity/tags plus problems, root cause, evidence, within a timeframe. The closing quote directly ties the tool to a high-level diagnostic question, distinguishing it from the Kubernetes/Dynatrace siblings.

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

  Usage Guidelines5/5

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

  Explicitly frames the intended question: 'This is the tool for "does the unhealthy deployment line up with a known Dynatrace problem".' It also routes the agent to find_dynatrace_entity when an entity ID is still needed, covering the prerequisite case.

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

• spawn_ephemeral_debug_containerA
  4.7/5.0
  Behavior5/5

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

  With no annotations, the description carries the full burden of behavior disclosure. It explicitly warns that the operation mutates the live pod spec and that the ephemeral container cannot be removed without restarting the pod. It also explains the sleep-for-duration behavior and the target_container namespace behavior, giving a complete behavioral picture.

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

  Conciseness5/5

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

  The description is compact and front-loaded: the core action comes first, followed by usage rationale, follow-up guidance, and the critical side-effect warning. Every sentence earns its place with no redundancy or fluff.

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

  Completeness5/5

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

  For a mutating tool with no annotations, the description covers the essential operational context: what it does, why you'd use it, how long it lives, how to interact with it afterward, and the irreversible side effect. The mention of the 'returned container name' also bridges to the output schema, making the tool callable end-to-end.

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

  Parameters4/5

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

  Schema description coverage is 0%, so the description must compensate. It adds real meaning to duration_seconds (container sleeps for that long), target_container (shares its process namespace), and image (provides tools the app image lacks). Less critical parameters like pod, namespace, and initial_command are self-explanatory from their names/defaults.

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

  Purpose5/5

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

  The description uses a specific verb-resource pair ('Attach an ephemeral debug container to a running pod') and names the mechanism ('kubectl debug'). It gives a concrete motivating example (distroless/minimal image lacking tools) and is clearly distinguishable from sibling tools like exec_in_pod, which runs in an existing container.

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

  Usage Guidelines4/5

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

  The description clearly states when to use it ('e.g. to get a shell with tools a distroless/minimal app image lacks') and tells the agent to follow up with exec_in_pod using the returned container name. It does not explicitly state when not to use it, but the use case and warning about mutating the pod imply a selective, cautious trigger.

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