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pvc_get

Retrieve detailed information about a specific PersistentVolumeClaim in Kubernetes, including its configuration and status, by specifying the context, namespace, and claim name.

Instructions

Get details of a specific PersistentVolumeClaim.

Args: context_name: The Kubernetes context name namespace: The Kubernetes namespace name: The PersistentVolumeClaim name

Returns: Detailed information about the PersistentVolumeClaim

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
context_nameYes
namespaceYes
nameYes

Implementation Reference

  • The main handler function for the 'pvc_get' tool. It fetches detailed information about a specific PersistentVolumeClaim (PVC) in a Kubernetes namespace using the CoreV1Api. The function is decorated with @mcp.tool() which registers it as an MCP tool.
    @mcp.tool()
    @use_current_context
    def pvc_get(context_name: str, namespace: str, name: str):
        """
        Get details of a specific PersistentVolumeClaim.
    
        Args:
            context_name: The Kubernetes context name
            namespace: The Kubernetes namespace
            name: The PersistentVolumeClaim name
    
        Returns:
            Detailed information about the PersistentVolumeClaim
        """
        core_v1: CoreV1Api = get_api_clients(context_name)["core"]
        pvc = core_v1.read_namespaced_persistent_volume_claim(name=name, namespace=namespace)
        return {
            "name": pvc.metadata.name,
            "status": pvc.status.phase,
            "storage": pvc.spec.resources.requests.get("storage"),
            "access_modes": pvc.spec.access_modes,
            "storage_class": pvc.spec.storage_class_name
        }
Behavior2/5

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 states the tool retrieves details (implying read-only), but doesn't disclose behavioral traits like error handling (e.g., what happens if the PVC doesn't exist), authentication needs, rate limits, or the format of returned details. For a read operation with zero annotation coverage, this leaves significant gaps.

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: the first sentence states the purpose clearly, followed by structured Args and Returns sections. There's no wasted text, though the Returns section is vague ('Detailed information')—this is a completeness issue, not conciseness. The structure aids readability without being verbose.

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?

Given the tool's moderate complexity (3 required parameters, no output schema, no annotations), the description is incomplete. It lacks details on error conditions, output format, and usage context (e.g., how it differs from pv_get). While it covers the basic purpose and parameters, it doesn't provide enough information for reliable agent invocation in a Kubernetes environment with many sibling tools.

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 0%, so the schema provides no parameter descriptions. The description lists the three parameters with brief explanations (e.g., 'The Kubernetes context name'), adding basic semantics beyond the schema's titles. However, it doesn't elaborate on format, constraints, or examples (e.g., valid namespace patterns), leaving room for improvement. With 0% coverage, this partial compensation justifies a baseline 3.

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 with a specific verb ('Get details') and resource ('PersistentVolumeClaim'). It distinguishes itself from siblings like pvc_list (which lists multiple) and pvc_create/delete/update (which modify). However, it doesn't explicitly contrast with pv_get (which gets PersistentVolumes) or other *_get tools, keeping it at a 4 rather than 5.

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 when to choose pvc_get over pvc_list (for details of a specific claim vs. listing all), nor does it reference prerequisites like needing the PVC to exist. The agent must infer usage from the tool name and parameters alone.

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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