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xiaoyuchenhot

MCP Multi-Tool Server

multiply

Calculate the product of two numbers by multiplying them together. Use this tool to perform multiplication operations with specified numerical inputs.

Instructions

Multiply two numbers together.

Args:
    a: The first number
    b: The second number

Returns:
    The product of a and b

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
aYes
bYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • server.py:56-68 (handler)
    The 'multiply' tool handler function that multiplies two float inputs. Registered via @mcp.tool() decorator, with schema inferred from type annotations and docstring.
    @mcp.tool()
    def multiply(a: float, b: float) -> float:
        """
        Multiply two numbers together.
        
        Args:
            a: The first number
            b: The second number
        
        Returns:
            The product of a and b
        """
        return a * b
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. While it states the basic operation, it doesn't mention important behavioral traits like error handling (e.g., overflow), performance characteristics, or any constraints on input values. The description is minimal and lacks context beyond the basic operation.

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 well-structured with clear sections for Args and Returns. Every sentence earns its place, and the information is front-loaded with the core purpose stated first. No wasted words or redundancy.

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 simplicity (basic arithmetic operation with 2 parameters) and the presence of an output schema (which handles return value documentation), the description is reasonably complete. It covers the purpose, parameters, and return value adequately for this straightforward tool, though it could benefit from more behavioral context.

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?

With 0% schema description coverage, the description compensates by clearly explaining both parameters ('a' as 'the first number' and 'b' as 'the second number'). It adds meaningful semantics beyond what the bare schema provides, though it doesn't specify constraints like numeric types or ranges.

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

Purpose5/5

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

The description clearly states the specific verb ('multiply') and resource ('two numbers'), distinguishing it from siblings like 'add', 'divide', or 'subtract'. The first sentence directly explains what the tool does without being vague or tautological.

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 like 'add', 'divide', or 'power'. It doesn't mention any specific contexts, exclusions, or prerequisites for choosing multiplication over other mathematical operations.

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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