Mong MCP Server

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get_random_name

Generate Docker-style random names for containers, services, or projects using the MCP interface. This tool provides moby-like name generation functionality integrated with Claude Desktop and VS Code Copilot Agent.

Instructions

Generate a random name like Docker does.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_random_name"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
`result`	Yes

Implementation Reference

mong_mcp_server/server.py:12-14 (handler)
The handler function for the 'get_random_name' MCP tool. It executes the tool logic by calling the external mong.get_random_name() function to generate a random Docker-like name.
```
def get_random_name() -> str:
    """Tool to Generate Docker-Like Random Names"""
    return mong.get_random_name()
```
mong_mcp_server/server.py:8-11 (registration)
The registration of the 'get_random_name' tool using the FastMCP @tool decorator, specifying its name and description.
```
@mcp.tool(
    name="get_random_name",
    description="Generate a random name like Docker does."
)
```

Schema Changelog

Changes observed during successful MCP inspections.

First observedAug 15, 2025

TDQS

B3/5.0

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 mentions generating a name 'like Docker does,' which hints at a specific behavior or format, but doesn't disclose details like output format, randomness source, rate limits, or error handling. This leaves significant gaps in understanding how the tool behaves beyond its basic purpose.

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 no wasted words. It's front-loaded with the core purpose ('Generate a random name') and adds a clarifying analogy ('like Docker does'), making it appropriately sized and easy to parse.

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 low complexity (0 parameters, no annotations, but has an output schema), the description is minimally adequate. It states what the tool does but lacks details on behavior, output format, or usage context. The presence of an output schema means the description doesn't need to explain return values, but it could still benefit from more context about the 'Docker-like' naming convention.

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 tool has 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics, and it appropriately avoids discussing non-existent inputs, earning a baseline score of 4 for this context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool 'Generate[s] a random name like Docker does,' which provides a clear verb ('generate') and resource ('random name'). However, it's somewhat vague about what 'like Docker does' entails—whether it's a specific naming convention, format, or style—and there are no sibling tools to distinguish from, so it can't achieve the highest differentiation.

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 offers no guidance on when to use this tool versus alternatives, prerequisites, or exclusions. It implies usage for generating random names, but without context or sibling tools, there's no explicit when/when-not advice, leaving the agent with minimal direction.

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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