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kelseyee

Calculator MCP

by kelseyee

factorial

Calculate the factorial of an integer by multiplying all positive integers up to that number. Use this tool to compute mathematical permutations and combinations.

Instructions

计算整数阶乘

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The 'factorial' tool handler, decorated with @mcp.tool() for registration in FastMCP. Computes the factorial of an integer n using math.factorial(n). Includes input type hint (int) and output (int), with docstring describing the function.
    @mcp.tool()
    def factorial(n: int) -> int:
        """计算整数阶乘"""
        return math.factorial(n)
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. '计算整数阶乘' only states what the tool does, not how it behaves - no information about input constraints (negative numbers, large values), performance characteristics, error handling, or output format. The description is minimal and lacks behavioral context.

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 extremely concise - a single phrase that directly states the tool's purpose. There's no wasted language or unnecessary elaboration, making it efficiently front-loaded with the essential information.

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?

For a simple mathematical function with one parameter and an output schema, the description is minimally adequate. However, it lacks important context about input constraints and behavioral characteristics that would be helpful for an AI agent, especially given the complete absence of annotations.

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?

With 0% schema description coverage for the single parameter 'n', the description doesn't add any parameter-specific information beyond what's implied by the tool name. The schema shows 'n' is a required integer, but the description doesn't clarify valid ranges, constraints, or what happens with edge cases like n=0 or negative n.

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 '计算整数阶乘' (calculates integer factorial) clearly states the verb (calculates) and resource (integer factorial), making the purpose immediately understandable. However, it doesn't distinguish this tool from its mathematical siblings like 'power' or 'multiply' beyond stating it's specifically for factorial calculations.

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. While the mathematical operation is clear, there's no mention of when factorial calculation is appropriate compared to other mathematical operations available in the sibling tools list.

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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