OpenShift MCP Server

oc_explain

Explain OpenShift/Kubernetes resource schemas and API documentation by specifying resource type, API version, and field path. Get plaintext or JSON output, with recursive field details.

Instructions

Explain OpenShift/Kubernetes resource schemas, fields, and API documentation

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": {
      "resource": "<resource>"
    },
    "name": "oc_explain"
  }
}

Input Schema

Name	Required	Description	Default
`field`	No	Specific field path to explain (e.g., spec.containers, metadata.labels)
`output`	No	Output format for the explanation	plaintext
`context`	No	OpenShift context to use (optional)
`resource`	Yes	Resource type to explain (e.g., pod, deployment, service, route.route.openshift.io)
`recursive`	No	Show all fields recursively
`apiVersion`	No	Specific API version to explain (e.g., apps/v1, route.openshift.io/v1)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.2.0Aug 7, 2026

TDQS

B3.1/5.0

Behavior2/5

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

No annotations are provided, so the description bears the full burden. It only states the high-level purpose and does not disclose whether the operation is read-only, what output formats are supported, or any side effects. This is minimal behavioral disclosure beyond the name and purpose.

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 a single, front-loaded sentence with no fluff. It effectively communicates the tool's core purpose, though it could be enhanced with a second sentence about usage without adding much length.

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

Completeness2/5

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

The tool has 6 parameters and no output schema, yet the description provides only a high-level purpose. It does not mention the return format, how field paths work, or how it interacts with the OpenShift API. Given the tool's complexity, this is insufficient for an agent to fully understand its capabilities.

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?

All parameters have detailed descriptions in the input schema (100% coverage). The description adds no parameter-specific information, so the baseline of 3 applies. The schema adequately explains the parameters.

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 purpose: explaining OpenShift/Kubernetes resource schemas, fields, and API documentation. It uses a specific verb and resource, and the scope is clear, though it does not explicitly differentiate from sibling tools like oc_describe or oc_api_resources.

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 usage context is implied by the purpose: when you need to understand schemas or fields, this tool is the choice. However, there is no explicit guidance on when to use this vs alternatives, no exclusions, and no mention of specific use cases.

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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