Kubernetes Tools MCP Server

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose targeting specific Kubernetes resources and operations. The tools are well-differentiated by resource type (deployments, pods, services, nodes, namespaces) and action type (summaries, logs, events, specs), with no overlapping functionality that would cause confusion.
Naming Consistency5/5
All tools follow a consistent 'verb_noun' pattern with snake_case throughout. The naming convention is perfectly uniform with 'get_' prefix for all retrieval operations followed by specific resource identifiers, making the tool set highly predictable and readable.
Tool Count5/5
With 9 tools, this server is well-scoped for Kubernetes operations. Each tool earns its place by covering essential read-only operations for core Kubernetes resources (pods, deployments, services, nodes, namespaces), providing comprehensive monitoring capabilities without being overwhelming.
Completeness3/5
For a Kubernetes monitoring/read-only server, the coverage is good but has notable gaps. While it provides excellent read operations for core resources, there are no tools for creating, updating, or deleting resources, and missing operations like scaling deployments or executing commands in pods limit agent capabilities for full Kubernetes management.
Average 4.3/5 across 9 of 9 tools scored. Lowest: 3.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 3 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses behavioral traits: it retrieves logs, returns a string or None if no logs found, and raises specific errors (K8sConfigError, K8sApiError). However, it lacks details on permissions needed, rate limits, log format, or whether it's read-only/destructive (though 'retrieves' implies read-only). The error information is valuable but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded with the purpose in the first sentence. The Args and Returns sections are structured clearly, but the 'Raises' section could be integrated more concisely. Overall, it's efficient with minimal waste, though minor improvements in flow are possible.
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 the tool's moderate complexity (3 parameters, no annotations, but with output schema), the description is fairly complete. It covers parameters, return values, and errors. The output schema exists, so it doesn't need to explain return values in detail. However, it lacks context on log scope (e.g., time range, tailing) and permissions, which would enhance completeness.
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 description adds significant meaning beyond the input schema, which has 0% schema description coverage. It explains each parameter's purpose: namespace, pod_name, and container_name (with default behavior). This compensates well for the schema's lack of descriptions, though it doesn't detail format constraints (e.g., namespace/pod naming rules).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 logs from a Kubernetes pod and container.' This is a specific verb+resource combination that distinguishes it from sibling tools like get_pod_summaries or get_pod_events. However, it doesn't explicitly differentiate from all siblings (e.g., get_pod_container_statuses might overlap in context).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 sibling tools like get_pod_events (which might provide event logs) or clarify if this is for real-time vs. historical logs. The only implied usage is from the purpose statement, but no explicit when/when-not or alternative recommendations are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 and does well by disclosing behavioral traits: it's a read operation (implied by 'Get'), specifies error conditions (K8sConfigError, K8sApiError), and details the return structure. It doesn't mention rate limits or authentication needs, but covers key operational aspects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized but not optimally front-loaded: the purpose is clear upfront, but the detailed return values (which could be in an output schema) make it lengthy. Every sentence adds value (e.g., error explanations), but structure could be tighter by focusing more on usage context.
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?
Given the tool's moderate complexity (2 parameters, no annotations, but with an output schema), the description is complete: it explains purpose, parameters, returns (with detailed field descriptions), and errors. The output schema existence means return values are well-documented, making this description thorough for agent use.
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 meaning by explaining parameters: pod_name is 'Name of the pod to retrieve container statuses for' and namespace is 'Namespace of the pod (default is "default")'. This clarifies usage beyond the bare schema, though it doesn't detail format constraints (e.g., Kubernetes naming rules).
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 tool's purpose with a specific verb ('Get') and resource ('status for all containers in a specified Kubernetes pod'), distinguishing it from siblings like get_pod_summaries (general pod info) or get_logs_for_pod_and_container (logs rather than status). It precisely defines what the tool retrieves.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying the target (Kubernetes pod containers) but does not explicitly state when to use this tool versus alternatives like get_pod_summaries or get_pod_spec. It provides context (e.g., for monitoring container health) but lacks explicit guidance on tool selection or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 and does so well by detailing return values, key fields, and error conditions (K8SConfigError, K8sApiError). It explains that the spec is converted from V1PodSpec to a dictionary and lists common values for fields like restart_policy and dns_policy, adding valuable behavioral context beyond basic functionality.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded with the core purpose, followed by structured sections for Args, Returns, and Raises. Every sentence adds value, such as detailing return fields and errors, though the Returns section is somewhat lengthy but necessary for clarity.
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?
Given the tool's complexity (retrieving detailed pod specs) and the presence of an output schema (implied by the detailed Returns section), the description is complete enough. It covers purpose, parameters, return semantics with key fields, and error handling, providing all necessary context for an AI agent to use the tool effectively without redundancy.
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, which it does by explaining both parameters: pod_name as 'the name of the pod' and namespace as 'the namespace the pod belongs to (defaults to "default")'. This adds clear meaning beyond the schema's titles, though it doesn't elaborate on format constraints or examples.
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 'retrieves' and the resource 'spec for a given pod in a specific namespace', making the purpose specific and unambiguous. It distinguishes this tool from siblings like get_pod_summaries or get_pod_events by focusing on the detailed spec object rather than summaries or events.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying it retrieves a pod's spec, but it does not explicitly state when to use this tool versus alternatives like get_pod_summaries for high-level info or get_pod_container_statuses for container states. No explicit exclusions or prerequisites are provided, leaving usage context inferred rather than guided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing the return format (list of NamespaceSummary objects with specific fields), error conditions (K8sConfigError, K8sApiError), and behavioral aspects like what happens on API failure. It doesn't mention rate limits, authentication needs, or pagination behavior, but provides substantial 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (Parameters, Returns, Raises), front-loaded with the core purpose, and every sentence earns its place by providing essential information about behavior, output format, and error conditions without redundancy.
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?
Given the tool has no parameters, has an output schema (implied by the detailed Returns section), and no annotations, the description provides complete context: clear purpose, detailed return format with field descriptions, specific error conditions, and operational behavior. Nothing essential appears missing for this type of read-only listing tool.
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?
With 0 parameters and 100% schema coverage, the baseline would be 4. The description explicitly states 'This function does not take any parameters' in the Parameters section, which adds clarity beyond what the empty schema alone conveys, confirming this is a parameterless operation.
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 tool's purpose with specific verb ('Return') and resource ('summary of the namespaces for this Kubernetes cluster'), and distinguishes it from siblings by specifying it returns namespace summaries rather than deployments, pods, services, etc. The kubectl analogy provides helpful context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through the kubectl analogy and by specifying what it returns (namespace summaries), but doesn't explicitly state when to use this tool versus alternatives like get_pod_summaries or get_service_summaries. No explicit when-not-to-use guidance or prerequisite information is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 and does so well by disclosing behavioral traits: it specifies the kubectl-like behavior, lists potential errors (K8sConfigError, K8sApiError), and describes the return format. It does not mention rate limits or auth needs, but covers key operational aspects.
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 appropriately sized and front-loaded: the first sentence states the purpose clearly, followed by organized sections (Parameters, Returns, Raises) that add necessary detail without redundancy. Every sentence earns its place by providing essential information.
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?
Given the tool's complexity (1 parameter, no annotations, but with an output schema), the description is complete enough. It explains the purpose, parameter usage, return values in detail (though the output schema covers this, the description adds clarity), and error conditions, leaving no significant gaps for an AI agent.
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 description adds significant meaning beyond the input schema, which has 0% coverage. It explains the 'namespace' parameter's purpose (to list deployments from a specific namespace or all namespaces), default behavior (None for all namespaces), and semantics, compensating fully for the schema's lack of 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?
The description clearly states the verb 'retrieves' and resource 'list of DeploymentSummary objects for deployments', specifying it works 'in a given namespace or all namespaces'. It distinguishes from siblings by focusing on deployments rather than pods, nodes, services, etc., and explicitly mentions the kubectl analogy for context.
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 provides clear context for when to use this tool (to get deployment summaries) and implies when not to use it (e.g., for pod or service summaries, as indicated by sibling tool names). However, it does not explicitly name alternatives or state exclusions, such as when to use get_pod_summaries instead.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing behavioral traits: it specifies the return format (list of NodeSummary with detailed fields), mentions exceptions (K8sConfigError, K8sApiError), and implies read-only behavior through 'Return a summary'. It doesn't cover rate limits or auth needs, but adds significant context beyond basic purpose.
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 well-structured with clear sections (Parameters, Returns, Raises), front-loaded purpose, and no wasted sentences. Every part adds value: the kubectl comparison sets context, the parameter note is essential, and the return details are comprehensive but necessary.
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?
Given 0 parameters, no annotations, but a detailed output schema in the description, the description is complete. It covers purpose, usage hint, parameter semantics, return format, and exceptions, making it fully adequate for this tool's complexity without redundancy.
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 description explicitly states 'This function does not take any parameters', which adds clarity beyond the empty input schema. With 0 parameters and 100% schema coverage, the baseline is 4, and this confirmation earns it without needing further compensation.
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 ('Return a summary') and resource ('nodes for this Kubernetes cluster'), and explicitly distinguishes it from siblings by comparing to `kubectl get nodes -o wide`, which helps differentiate from other node-related tools like get_pod_summaries or get_service_summaries.
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 provides clear context by mentioning it's similar to `kubectl get nodes -o wide`, which implies usage for high-level node overviews. However, it doesn't explicitly state when to use this versus alternatives like get_pod_summaries or get_deployment_summaries, nor does it mention exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing behavioral traits: it specifies the return type (list of PodSummary), documents potential exceptions (K8sConfigError, K8sApiError), and describes error conditions. However, it doesn't mention rate limits, authentication needs, or side effects like caching.
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 well-structured with clear sections (Parameters, Returns, Raises), front-loaded with the core purpose, and every sentence earns its place by providing essential information without redundancy. It's appropriately sized for a tool with one parameter and detailed return documentation.
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?
Given the tool's moderate complexity, no annotations, and the presence of a detailed output schema (which the description references), the description is complete: it covers purpose, parameter semantics, return structure, and error conditions. The output schema handles return value details, so the description appropriately focuses on higher-level context.
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 fully documents the single parameter 'namespace', explaining its optional nature, default value (None), and semantic meaning ('specific namespace to list pods from' vs 'all namespaces'). This adds significant value beyond the bare schema.
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 'Retrieves' and resource 'list of PodSummary objects for pods', specifying it works 'in a given namespace or all namespaces'. It distinguishes from siblings like get_pod_container_statuses (container-level details) and get_pod_events (event logs) by focusing on summary-level pod status.
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 provides clear context by specifying it retrieves summaries for pods in a namespace or all namespaces, but does not explicitly state when to use this tool versus alternatives like get_pod_container_statuses or get_pod_events. The namespace parameter guidance implies usage but lacks explicit sibling comparison.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 and does well by detailing the kubectl equivalent (clarifying the query logic), listing return fields with types, and specifying error conditions (K8sConfigError, K8sApiError). It doesn't cover rate limits or auth needs, but gives solid 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with clear sections (description, parameters, returns, raises), front-loaded purpose, and no wasted sentences. Each part adds value, such as the kubectl analogy and detailed return field explanations.
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?
Given the tool's moderate complexity (2 params, no annotations), the description is highly complete: it explains the action, parameters, return structure (with output schema details), and error cases. The output schema is present, so no need to redundantly explain returns.
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%, so the description must compensate fully. It explicitly documents both parameters (pod_name, namespace), including the namespace default value ('default') and their purposes, adding crucial meaning beyond the bare schema.
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 specific action ('Get events for a specific Kubernetes pod') and resource ('pod'), distinguishing it from siblings like get_pod_summaries or get_logs_for_pod_and_container by focusing exclusively on events. The kubectl command analogy reinforces the precise scope.
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 implicitly suggests usage when needing pod events, but lacks explicit guidance on when to choose this over alternatives like get_pod_summaries (which might include event data) or get_logs_for_pod_and_container (for logs vs. events). 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by detailing return values, error conditions (K8sConfigError, K8sApiError), and the kubectl analogy for context. It lacks explicit rate limits or auth requirements, but covers key behavioral aspects adequately.
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 well-structured with clear sections (Parameters, Returns, Raises), front-loaded purpose, and no wasted sentences. Each part adds value, making it efficient and easy to parse.
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?
Given the tool's moderate complexity, no annotations, and an output schema present, the description is complete: it covers purpose, parameters, returns in detail (including field explanations), error cases, and contextual analogy, leaving no significant gaps for agent understanding.
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%, so the description must compensate fully. It does so by clearly explaining the namespace parameter's purpose, default behavior (None lists all namespaces), and effect, adding essential meaning beyond the bare schema.
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 specific action ('Retrieves a list of ServiceSummary objects') and resource ('services in a given namespace or all namespaces'), with explicit differentiation from siblings through the kubectl analogy and focus on services rather than deployments, pods, nodes, etc.
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 provides clear context for when to use this tool (to get service summaries, with namespace filtering) and implicitly distinguishes it from siblings by focusing on services, but does not explicitly state when to use alternatives like get_deployment_summaries or get_pod_summaries for other resource types.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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