Skip to main content
Glama

daemonset_delete

Remove a DaemonSet from a Kubernetes namespace to manage cluster resources and configurations.

Instructions

Delete a DaemonSet from the specified namespace.

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

Returns: Status of the deletion operation

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
context_nameYes
namespaceYes
nameYes

Implementation Reference

  • The daemonset_delete tool handler function that deletes a DaemonSet in the specified namespace using the Kubernetes AppsV1Api.
    def daemonset_delete(context_name: str, namespace: str, name: str):
        """
        Delete a DaemonSet from the specified namespace.
    
        Args:
            context_name: The Kubernetes context name
            namespace: The Kubernetes namespace
            name: The DaemonSet name
    
        Returns:
            Status of the deletion operation
        """
        apps_v1: AppsV1Api = get_api_clients(context_name)["apps"]
        apps_v1.delete_namespaced_daemon_set(name=name, namespace=namespace)
        return {"name": name, "status": "Deleted"}
  • Registration of the daemonset_delete tool via the @mcp.tool() decorator, with current context and readonly permission checks.
    @mcp.tool()
    @use_current_context
    @check_readonly_permission
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 of behavioral disclosure. While 'Delete' implies a destructive mutation, the description doesn't specify whether this deletion is permanent, requires confirmation, has side effects (e.g., terminating pods), or what permissions are needed. 'Returns: Status of the deletion operation' is vague—it doesn't indicate success/failure indicators or error conditions. For a destructive tool with zero annotation coverage, this is inadequate.

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 core purpose, followed by structured sections for arguments and returns. There's minimal fluff, though the 'Args' and 'Returns' labels are slightly redundant given the schema. Every sentence earns its place, but the structure could be more integrated (e.g., merging with schema fields).

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 complexity (a destructive Kubernetes operation), lack of annotations, and no output schema, the description is incomplete. It doesn't address critical context: safety warnings, idempotency (what happens if the DaemonSet doesn't exist?), required RBAC permissions, or what the 'Status' return entails. For a tool that permanently removes resources, this leaves significant gaps for an AI agent to operate safely and effectively.

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 clearly documents all three parameters with brief explanations, adding meaningful context beyond the schema (which has 0% description coverage). Each parameter's purpose is stated concisely, though it doesn't elaborate on format constraints (e.g., valid namespace names) or dependencies between parameters. Since schema coverage is 0%, the description compensates well but not fully (e.g., no examples or edge cases).

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 action ('Delete') and resource ('a DaemonSet from the specified namespace'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling deletion tools like 'deployment_delete' or 'pod_delete', which follow the same pattern but target different Kubernetes resources.

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 prerequisites (e.g., needing appropriate permissions), when deletion is appropriate versus other operations, or how it relates to sibling tools like 'daemonset_get' or 'daemonset_list' that might be used first. The only implicit context is that you need a DaemonSet to delete.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/bourbonkk/k8s-pilot'

If you have feedback or need assistance with the MCP directory API, please join our Discord server