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inesaranab

Tavily Web Search MCP Server

by inesaranab

roll_dice

Simulate dice rolls using standard notation to generate random numbers for games, decisions, or probability calculations.

Instructions

Roll the dice with the given notation

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
notationYes
num_rollsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • server.py:22-27 (handler)
    MCP tool handler for 'roll_dice' that creates a DiceRoller instance and returns its string representation containing the roll results.
    @mcp.tool()
    def roll_dice(notation: str, num_rolls: int = 1) -> str:
        """Roll the dice with the given notation"""
        roller = DiceRoller(notation, num_rolls)
        return str(roller)
  • DiceRoller class implementing the dice rolling logic: parses notation like '2d20k1', rolls random dice, keeps highest if specified, supports multiple rolls, and formats output for the tool.
    class DiceRoller:
        def __init__(self, notation, num_rolls=1):
            self.notation = notation
            self.num_rolls = num_rolls
            self.dice_pattern = re.compile(r"(\d+)d(\d+)(k(\d+))?")
    
        def roll_dice(self):
            match = self.dice_pattern.match(self.notation)
            if not match:
                raise ValueError("Invalid dice notation")
    
            num_dice = int(match.group(1))
            dice_sides = int(match.group(2))
            keep = int(match.group(4)) if match.group(4) else num_dice
    
            rolls = [random.randint(1, dice_sides) for _ in range(num_dice)]
            rolls.sort(reverse=True)
            kept_rolls = rolls[:keep]
    
            return rolls, kept_rolls
    
        def roll_multiple(self):
            """Roll the dice multiple times according to num_rolls"""
            results = []
            for _ in range(self.num_rolls):
                rolls, kept_rolls = self.roll_dice()
                results.append({
                    "rolls": rolls,
                    "kept": kept_rolls,
                    "total": sum(kept_rolls)
                })
            return results
    
        def __str__(self):
            if self.num_rolls == 1:
                rolls, kept_rolls = self.roll_dice()
                return f"ROLLS: {', '.join(map(str, rolls))} -> RETURNS: {sum(kept_rolls)}"
            else:
                results = self.roll_multiple()
                result_strs = []
                for i, result in enumerate(results, 1):
                    result_strs.append(f"Roll {i}: ROLLS: {', '.join(map(str, result['rolls']))} -> RETURNS: {result['total']}")
                return "\n".join(result_strs)
  • server.py:22-27 (registration)
    Registration of the 'roll_dice' tool using FastMCP @mcp.tool() decorator.
    @mcp.tool()
    def roll_dice(notation: str, num_rolls: int = 1) -> str:
        """Roll the dice with the given notation"""
        roller = DiceRoller(notation, num_rolls)
        return str(roller)
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 action ('roll the dice') but doesn't disclose behavioral traits like whether this is deterministic or random, what the output format is, or any limitations. The description is minimal and lacks essential context for a tool that likely involves randomness.

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 with no wasted words. It's appropriately sized for a simple tool and front-loaded with the core action. Every word earns its place, making it highly concise.

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 and the presence of an output schema, the description is somewhat complete but inadequate. It covers the basic action but lacks details on notation format and behavioral aspects. With no annotations and minimal parameter explanation, it doesn't fully compensate for the gaps, though the output schema might help.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It mentions 'given notation' which hints at the 'notation' parameter, but doesn't explain what the notation entails. It doesn't address the 'num_rolls' parameter at all. The description adds minimal meaning beyond the schema, failing to clarify parameter usage.

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

Purpose3/5

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

The description 'Roll the dice with the given notation' states a clear action (roll) and resource (dice), but it's vague about what 'given notation' means. It doesn't distinguish from sibling tools like text_to_speech or web_search, but those are unrelated, so differentiation isn't critical here. The purpose is understandable but lacks specificity about the dice notation format.

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?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention any context, prerequisites, or exclusions. Given the unrelated sibling tools, explicit alternatives aren't needed, but there's no indication of typical use cases or constraints.

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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