Skip to main content
Glama
nykznykz

Example MCP Server

by nykznykz

greet

Generate personalized greeting messages by providing a name. This tool creates friendly welcome messages for users in applications.

Instructions

Generate a friendly greeting message.

Args: name: The name of the person to greet

Returns: A personalized greeting message

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The main handler for the 'greet' MCP tool, registered via @mcp.tool() decorator. It receives the name parameter and delegates to the _greet helper function.
    @mcp.tool()
    def greet(name: str) -> str:
        """
        Generate a friendly greeting message.
    
        Args:
            name: The name of the person to greet
    
        Returns:
            A personalized greeting message
        """
        return _greet(name)
  • Pydantic BaseModel defining the input schema for the 'greet' tool request.
    class GreetRequest(BaseModel):
        """Request model for greet tool."""
    
        name: str
  • Internal helper function containing the core logic for generating the greeting message.
    def _greet(name: str) -> str:
        """
        Generate a friendly greeting message.
    
        Args:
            name: The name of the person to greet
    
        Returns:
            A personalized greeting message
        """
        return f"Hello, {name}! Welcome to the MCP server."
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 generates a greeting and returns a message, but doesn't describe any behavioral traits such as rate limits, error handling, or side effects. For a tool with zero annotation coverage, this is a significant gap in transparency.

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 appropriately sized and front-loaded: it starts with the core purpose in the first sentence, followed by clear sections for arguments and returns. Every sentence earns its place without waste, making it efficient and well-structured for quick understanding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (one parameter, simple output) and the presence of an output schema (which handles return values), the description is complete enough. It covers the purpose, parameter meaning, and return type, though it lacks behavioral details. For a straightforward greeting tool, this is largely sufficient, but minor gaps prevent a perfect score.

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 schema description coverage is 0%, but the description compensates by explaining the parameter 'name' as 'The name of the person to greet.' This adds meaning beyond the schema, which only specifies the type. However, with only one parameter and no complex details, the description provides adequate but minimal semantic context, aligning with the baseline for this simple case.

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: 'Generate a friendly greeting message.' It specifies the verb ('generate') and resource ('greeting message'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from its sibling tool 'calculate', which is a different function, so it doesn't reach the highest 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 any context, prerequisites, or exclusions for usage. The only implied usage is for generating greetings, but this is basic and lacks explicit when/when-not instructions or named alternatives.

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/nykznykz/mcp_example'

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