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Example MCP Server

Example MCP Server

by Joseph19820124
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

magic_word

Generate random words for creative writing, brainstorming, or vocabulary building using this MCP server tool.

Instructions

Return a random word from the list

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": "magic_word"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • main.py:32-34 (handler)
    The handler function for the 'magic_word' tool, which selects and returns a random word from the predefined magic_words list.
    def magic_word() -> str:
    """Return a random word from the list"""
    return random.choice(magic_words)
  • main.py:31-31 (registration)
    Registers the magic_word tool with the FastMCP server using the @mcp.tool decorator.
    @mcp.tool
  • main.py:5-5 (helper)
    Predefined list of magic words used by the magic_word tool handler.
    magic_words = ["consistent", "honor", "attitude", "motivation", "perseverance", "integrity", "optimistic", "never giving up"]

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.1/5.0
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 states the tool returns a random word, implying a read-only operation, but doesn't disclose behavioral traits like whether the word list is fixed, how randomness is seeded, or if there are any rate limits or permissions needed. This leaves significant gaps in understanding the tool's behavior.

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, clear sentence with no wasted words, making it highly concise and front-loaded. Every part of the sentence contributes directly to explaining the tool's purpose.

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 has no annotations, no output schema, and 0 parameters, the description is minimal. It states what the tool does but lacks context about the word list source, randomness behavior, or expected output format, making it incomplete for effective use without additional assumptions.

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 input schema has 0 parameters with 100% coverage, so no parameter information is needed. The description doesn't add parameter details, which is appropriate, earning a baseline score of 4 as it doesn't need to compensate for any schema gaps.

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 action ('Return') and resource ('a random word from the list'), making the purpose understandable. However, it doesn't distinguish this tool from its mathematical siblings (add, divide, multiply, subtract), which suggests a different domain, so it lacks explicit sibling 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 provides no guidance on when to use this tool versus alternatives or in what context it's appropriate. Given the sibling tools are mathematical operations, there's an implied contrast, but no explicit usage instructions are provided.

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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