Skip to main content
Glama

deployment_get

Retrieve detailed information about a specific Kubernetes deployment, including status and configuration, to monitor and manage application deployments across clusters.

Instructions

Get details of a specific Deployment.

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

Returns: Detailed information about the Deployment

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
context_nameYes
namespaceYes
nameYes

Implementation Reference

  • Implementation of the deployment_get MCP tool handler. This function is decorated with @mcp.tool() for registration and @use_current_context. It retrieves detailed information about a specific Kubernetes Deployment (name, replicas, labels, containers) in the given namespace using the AppsV1Api client.
    @mcp.tool()
    @use_current_context
    def deployment_get(context_name: str, namespace: str, name: str):
        """
        Get details of a specific Deployment.
    
        Args:
            context_name: The Kubernetes context name
            namespace: The Kubernetes namespace
            name: The Deployment name
    
        Returns:
            Detailed information about the Deployment
        """
        apps_v1: AppsV1Api = get_api_clients(context_name)["apps"]
        deployment = apps_v1.read_namespaced_deployment(name=name, namespace=namespace)
        return {
            "name": deployment.metadata.name,
            "replicas": deployment.spec.replicas,
            "labels": deployment.metadata.labels,
            "containers": [c.image for c in deployment.spec.template.spec.containers]
        }
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. It states the tool retrieves details, implying a read-only operation, but doesn't clarify aspects like error handling (e.g., what happens if the deployment doesn't exist), authentication needs, rate limits, or the format of the returned details. This is a significant gap for a tool with zero annotation coverage.

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, starting with the core purpose followed by parameter and return sections. Each sentence serves a clear function, with no wasted words. Minor improvements could include integrating the parameter explanations more seamlessly, but overall it's efficient.

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 complexity (3 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavioral traits, error conditions, and the structure of the returned 'Detailed information,' which is crucial for an agent to use the tool effectively. This leaves significant gaps in understanding.

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?

The description lists the three parameters with brief explanations (e.g., 'The Kubernetes context name'), which adds some meaning beyond the schema's 0% description coverage. However, it doesn't provide detailed semantics, such as valid formats for 'context_name' or how 'name' relates to deployments. With low schema coverage, this partial compensation earns a baseline score.

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: 'Get details of a specific Deployment.' This is a specific verb ('Get') and resource ('Deployment'), making the function unambiguous. However, it doesn't differentiate from sibling tools like 'deployment_list' or 'deployment_update' beyond the basic operation type, which prevents a perfect score.

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 sibling tools like 'deployment_list' for listing deployments or 'deployment_update' for modifying them, nor does it specify prerequisites or contexts for usage. This leaves the agent without explicit direction on tool selection.

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