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DotnetFastMCP

DotnetFastMCP

by tekspry
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Hybrid

divide_numbers

Calculate the integer quotient of two numbers (a / b) for floor division. Returns the floor quotient and throws an error if divisor b is zero.

Instructions

Calculates the integer quotient of two numbers (a / b). Use this tool for integer floor division. Do not use for addition (use 'add_numbers' instead) or multiplication (use 'multiply_numbers' instead). Returns floor quotient; throws an error if divisor 'b' is zero.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "a": "<a>",
      "b": "<b>"
    },
    "name": "divide_numbers"
  }
}

Input Schema

NameRequiredDescriptionDefault
aYesThe dividend integer to divide.
bYesThe divisor integer to divide by. Must not be zero.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.1

TDQS

A4.3/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full behavioral burden, and it does disclose two meaningful traits: the result is a floor quotient (not true division or a float), and a zero divisor throws an error. It stops short of stating rounding behavior for negatives or the error type/shape, which would fully close the gap for a zero-annotation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short sentences, front-loaded with the core behavior before the routing exclusions. The 'Do not use for addition/multiplication' clause is mildly verbose given the names already imply scope, but it earns its place as routing guidance.

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?

For a two-integer arithmetic tool with no output schema and no annotations, the description covers purpose, return semantics, and failure mode – enough to invoke correctly. Only the negative-number rounding convention is unaddressed, a minor gap for this operation.

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?

Schema description coverage is 100% – both 'a' (dividend) and 'b' (divisor, must not be zero) are documented in the schema. The description restates the a / b relationship and the non-zero constraint on 'b' but adds no format or edge-case detail beyond the schema, so the baseline of 3 applies.

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?

States a specific verb and resource ('Calculates the integer quotient of two numbers') and immediately constrains the operation to integer floor division. It explicitly distinguishes itself from the sibling tools add_numbers and multiply_numbers, so an agent can route correctly without opening any schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives both when-to-use ('Use this tool for integer floor division') and explicit when-not-to-use guidance, naming the two sibling alternatives (add_numbers, multiply_numbers) that a confused agent might otherwise pick. Nothing is left to inference.

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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