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matracey

my-mcp-server

by matracey

hello

Generate personalized greeting messages by providing a name. This tool creates hello messages for use in AI assistant integrations and development workflows.

Instructions

A simple greeting tool that returns a hello message.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesName to greet

Implementation Reference

  • The handler function that executes the 'hello' tool logic. Takes a 'name' parameter and returns a greeting message in MCP content format.
    async ({ name }) => {
      return {
        content: [{ type: 'text' as const, text: `Hello, ${name}!` }],
      }
    }
  • Zod schema definition for the 'hello' tool input. Defines a 'name' parameter as a string with a description.
    {
      name: z.string().describe('Name to greet'),
    },
  • src/server.ts:14-25 (registration)
    Tool registration with the MCP server using server.tool(). Registers 'hello' tool with its name, description, schema, and handler function.
    server.tool(
      'hello',
      'A simple greeting tool that returns a hello message.',
      {
        name: z.string().describe('Name to greet'),
      },
      async ({ name }) => {
        return {
          content: [{ type: 'text' as const, text: `Hello, ${name}!` }],
        }
      }
    )
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 'returns a hello message', implying a read-only operation, but doesn't disclose any behavioral traits like error handling, rate limits, or response format. This leaves the agent with minimal context beyond the basic action.

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 that directly states the tool's function without any wasted words. It is appropriately sized and front-loaded, making it easy to parse quickly.

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 (one parameter, no annotations, no output schema), the description is minimally adequate. It covers the basic purpose but lacks details on usage, behavior, or output, leaving gaps that could hinder an agent's ability to use it effectively in varied contexts.

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 input schema has 100% description coverage, with the parameter 'name' documented as 'Name to greet'. The description doesn't add any meaning beyond this, such as format constraints or examples. With high schema coverage, the baseline is 3, as the schema does the heavy lifting.

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 as 'returns a hello message' with the verb 'returns' and resource 'hello message', making it specific. However, with no sibling tools, it cannot distinguish from alternatives, so it doesn't reach the highest score of 5.

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, prerequisites, or context. It only states what it does, not when or why to invoke it, which is a significant gap.

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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