k8s_service_list
List all Kubernetes services in a specified namespace for cluster management and troubleshooting.
Instructions
[SAFE] List Kubernetes services in a namespace
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| namespace | Yes | default |
List all Kubernetes services in a specified namespace for cluster management and troubleshooting.
[SAFE] List Kubernetes services in a namespace
| Name | Required | Description | Default |
|---|---|---|---|
| namespace | Yes | default |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full behavioral disclosure burden. It only includes '[SAFE]' indicating no side effects, but omits critical behavioral traits like authentication requirements, rate limits, returned data format, or pagination. This leaves significant gaps for a tool that likely produces a list.
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 very concise, using a single short sentence with a safety tag. No wasted words, but the brevity sacrifices completeness. It is appropriately front-loaded but could be structured with additional sections for behavior or examples.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has a single parameter and no output schema, the description should compensate by explaining what the tool returns (e.g., list of service names, statuses, etc.) and any limitations. It does not, leaving the agent with an incomplete picture of how to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage for its single parameter 'namespace'. The description adds no extra meaning beyond what the schema already provides (required, with default 'default'). It fails to explain the parameter's role or constraints in listing services, leaving the agent without helpful semantic 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 tool lists Kubernetes services in a namespace, with a verb ('list') and resource ('Kubernetes services'). It implicitly differentiates from sibling tools like k8s_pod_list by specifying 'services', but does not explicitly name alternatives, losing a point for full 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?
No guidance is provided on when to use this tool versus alternatives such as k8s_pod_list or k8s_deployment_list. The description only states the action without context for selection, which is insufficient for an AI agent to choose correctly among many similar sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/skyvanguard/infra-ops-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server