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abhijeetka
by abhijeetka

get_deployments

Retrieve all Kubernetes deployments in a specified namespace to monitor and manage application deployments within your cluster.

Instructions

Get all deployments in the specified namespace

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
namespaceNodefault

Implementation Reference

  • The handler function for the 'get_deployments' tool. It uses kubectl to fetch deployments in the specified namespace as JSON. The @mcp.tool() decorator registers it with the FastMCP server.
    @mcp.tool()
    async def get_deployments(namespace: str = "default") -> dict:
        """Get all deployments in the specified namespace"""
        try:
            cmd = ["kubectl", "get", "deployments", "-n", namespace, "-o", "json"]
            result = subprocess.run(cmd, capture_output=True, text=True, check=True)
            return json.loads(result.stdout)
        except subprocess.CalledProcessError as e:
            return {"error": f"Failed to get nodes: {str(e)}"}
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 mentions retrieving deployments but lacks details on permissions needed, rate limits, pagination, or response format. This is inadequate for a tool that likely interacts with a Kubernetes API.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero wasted words, clearly front-loaded with the core action. It's appropriately sized for a simple retrieval tool.

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 of Kubernetes operations, no annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't cover behavioral aspects like authentication, error handling, or return values, leaving significant gaps for an AI agent.

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 description must compensate. It adds meaning by specifying that the 'namespace' parameter filters deployments, but doesn't explain the default value 'default' or provide examples. This partial compensation justifies 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 verb ('Get') and resource ('deployments') with scope ('in the specified namespace'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_daemonsets' or 'get_statefulsets' that also retrieve Kubernetes resources, missing full sibling distinction.

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?

No guidance is provided on when to use this tool versus alternatives like 'get_pods' or 'get_services', nor any context about prerequisites or exclusions. The description only states what it does without usage context.

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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