Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Mong MCP Server generate a random name for my new container"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol (MCP) server implementation that provides a moby-like random name generator ("mong") for use with tools like Claude Desktop and VS Code Copilot Agent.
Exposes a moby-like random name generator through the MCP interface
Easy setup and configuration
Related MCP server: Dice MCP Server
Add this server under the mcpServers key of Claude Desktop configuration.
See the official document of the Model Context Protocol for details.
{
"mcpServers": {
"mong": {
"command": "uvx",
"args": [
"--from",
"git+https://github.com/toshihikoyanase/mong-mcp-server",
"mong-mcp"
]
}
}
}or
{
"mcpServers": {
"mong": {
"command": "uv",
"args": [
"--directory",
"/path/to/repository",
"run",
"python",
"-m",
"mong_mcp_server"
]
}
}
}Add this server under the servers key in your MCP configuration for VS Code.
For example, to enable the MCP server in your workspace, create a .vscode/mcp.json file as follows:
{
"servers": {
"mong": {
"type": "stdio",
"command": "uvx",
"args": [
"--from",
"git+https://github.com/toshihikoyanase/mong-mcp-server",
"mong-mcp"
]
}
}
}Clone the repository:
git clone https://github.com/toshihikoyanase/mong-mcp-server.git
cd mong-mcp-serverStart the server:
uv run python -m mong_mcp_serverRun the unit tests to verify the server functionality:
uv run python -m unittest discover tests/This project is licensed under the MIT License. See the LICENSE file for details.
Generate a random name like Docker does.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
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.
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.
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.
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.
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.
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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
get_random_nameScored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'get_random_name' has a clear and distinct purpose that cannot be confused with any other tool in this set.
Since there is only one tool, naming consistency is inherently perfect. The tool name 'get_random_name' follows a clear verb_noun pattern, and there are no other tools to compare it against for inconsistency.
A single tool is generally too few for most server purposes, as it offers minimal functionality and limits agent capabilities. For a server named 'Mong MCP Server', which might imply broader utility, one tool feels thin and under-scoped, even if the tool itself is well-defined.
The server's purpose is unclear from the name and single tool, but the tool 'get_random_name' suggests a domain of name generation. However, with only one tool, there are severe gaps: no ability to customize names, list generated names, or perform other related operations, making the surface incomplete for any meaningful workflow.
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