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daemonset_get

Retrieve detailed information about a specific Kubernetes DaemonSet by providing context, namespace, and name parameters to inspect deployment configurations.

Instructions

Get details of a specific DaemonSet.

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

Returns: Detailed information about the DaemonSet

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
context_nameYes
namespaceYes
nameYes

Implementation Reference

  • The daemonset_get tool handler function. Decorated with @mcp.tool() which likely handles registration. Retrieves DaemonSet details from Kubernetes API using AppsV1Api.
    @mcp.tool()
    @use_current_context
    def daemonset_get(context_name: str, namespace: str, name: str):
        """
        Get details of a specific DaemonSet.
    
        Args:
            context_name: The Kubernetes context name
            namespace: The Kubernetes namespace
            name: The DaemonSet name
    
        Returns:
            Detailed information about the DaemonSet
        """
        apps_v1: AppsV1Api = get_api_clients(context_name)["apps"]
        daemonset = apps_v1.read_namespaced_daemon_set(name=name, namespace=namespace)
        return {"name": daemonset.metadata.name, "labels": daemonset.metadata.labels, "containers": [c.image for c in daemonset.spec.template.spec.containers]}
  • The @mcp.tool() decorator registers the daemonset_get function as an MCP tool.
    @mcp.tool()
  • Input schema defined by function parameters with type hints and docstring describing args and return value.
    def daemonset_get(context_name: str, namespace: str, name: str):
        """
        Get details of a specific DaemonSet.
    
        Args:
            context_name: The Kubernetes context name
            namespace: The Kubernetes namespace
            name: The DaemonSet name
    
        Returns:
            Detailed information about the DaemonSet
        """
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves details but doesn't mention whether this is a read-only operation, what permissions are required, error handling, or the format of the returned information. This leaves significant gaps for an agent to understand the tool's behavior.

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 well-structured with clear sections for purpose, arguments, and returns. It's front-loaded with the main purpose and avoids unnecessary verbosity. However, the 'Returns' section is somewhat vague ('Detailed information'), which slightly reduces efficiency.

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

Completeness3/5

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 minimally adequate. It covers the purpose and parameters but lacks details on behavior, error cases, and output format, leaving the agent with incomplete context for reliable use.

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?

The description includes an 'Args' section that lists and briefly describes all three parameters (context_name, namespace, name), adding meaningful context beyond the input schema which has 0% description coverage. This compensates well for the schema's lack of descriptions, though it doesn't provide detailed examples or constraints.

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 ('DaemonSet'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'daemonset_list' or 'daemonset_delete', which would require a 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 like 'daemonset_list' for listing multiple DaemonSets or 'daemonset_get' versus other resource-specific get tools. It lacks any context about prerequisites or typical 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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