k8s-docs-mcp

search_k8s_docs

Search official Kubernetes documentation for concepts, policies, and architectures, returning authoritative sections and kubernetes.io URLs to answer Kubernetes questions.

Instructions

Searches official Kubernetes documentation (kubernetes.io) for concepts, policies, and architectures.

Args:
    query: Search keywords or question (e.g., 'network policies ingress rules', 'validating admission webhook cel').
    limit: Maximum number of documentation sections to return (default: 3).

Returns:
    Authoritative documentation sections with official kubernetes.io URLs.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "search_k8s_docs"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No
`query`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 20, 2026

TDQS

A3.6/5.0

Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It does disclose the data source (official kubernetes.io docs) and that results are authoritative sections with official URLs, which is meaningful context for trust. However it omits any mention of network dependency, latency, failure behavior, or result ranking/scope limits.

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 Args/Returns structure front-loads the purpose and keeps parameter info scannable; every line carries information. Slightly formulaic but no filler sentences.

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 2-parameter search tool with an output schema, the definition covers purpose, both params, and the nature of results. The remaining gap is input-side semantics (query syntax, limit bounds) rather than missing essentials.

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 is doing real work: it names both parameters, explains 'limit' as the maximum number of documentation sections to return (matching the schema default of 3), and gives concrete query examples. It stops short of guidance on limit ranges or query syntax nuance.

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?

States a specific verb and resource ('Searches official Kubernetes documentation (kubernetes.io)') plus the scope of content covered (concepts, policies, architectures). It doesn't explicitly distinguish itself from siblings, but the sibling names (list_supported_resources, get_canonical_manifest, check_api_version, sync_upstream_docs) are functionally distinct enough that confusion is unlikely.

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?

Usage is only implied: the example queries ('network policies ingress rules', 'validating admission webhook cel') suggest conceptual/policy questions, which hints at when this tool is appropriate over the sibling lookups. There is no explicit when-to-use, when-not-to-use, or named alternative.

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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