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ranson21

kube-diagnostics-mcp

by ranson21

get_service_overview

Diagnose Kubernetes workload health in one call: replicas, pod issues, warning events, resource usage vs limits, and golden signals.

Instructions

One-call digest for a service: replicas, pod problems, recent warning events, usage vs requests/limits, golden signals if available. The first tool to call for 'is X healthy?'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
serviceYesWorkload name as you would say it: a Deployment, StatefulSet, DaemonSet, Service, or Pod name
namespaceNoKubernetes namespace (required: no default configured; see list_namespaces)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries full disclosure responsibility. It explains what the tool returns (replicas, pod problems, events, usage, golden signals) and notes that golden signals are included 'if available', which is honest. However, it does not mention side effects (though it is clearly read-only), error conditions, pagination, or any rate limits. For a read-only digest, this is acceptable but not exhaustive.

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 long, front-loaded with the core purpose and contents, and includes a practical usage hint. Every word earns its place; there is no fluff or repetition of schema details.

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 tool is a high-level digest with no output schema and no annotations, so the description must explain return value semantics. It lists the major components (replicas, pod problems, events, usage, golden signals) and qualifies the golden signals as conditional. It does not specify formats or ordering, but for an entry-point health check this is reasonably complete.

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 100%, so the schema already documents both parameters: service and namespace. The description adds no extra parameter semantics beyond what the schema provides, so the baseline score of 3 is appropriate. It doesn't clarify types or formats beyond schema, but nothing more is needed.

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 ('get') and resource ('service overview') and enumerates the concrete contents (replicas, pod problems, warning events, usage vs limits, golden signals). It also explicitly positions itself as 'the first tool to call for is X healthy?', distinguishing it from sibling tools like get_pod_status or get_golden_signals, which focus on narrower slices.

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 the tool ('first tool to call for is X healthy?') and hints at its broad scope. It does not explicitly list when not to use it or name specific alternatives for deeper dives, but the context is strong enough for an agent to select it as an entry point.

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