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vinitv

Tavily Web Search MCP Server

by vinitv

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

Implementation Reference

  • server.py:25-29 (handler)
    The handler function for the 'roll_dice' MCP tool. It instantiates a DiceRoller with the provided notation and number of rolls, then returns its string representation containing the roll results. The @mcp.tool() decorator also handles registration and schema inference from the signature.
    @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)
  • The DiceRoller class provides the core logic for parsing dice notation (e.g., '2d6k1'), rolling dice, keeping highest rolls if specified, handling multiple rolls, and formatting the output. Used by the roll_dice tool handler.
    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)
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 but only states the basic action. It doesn't describe what the tool returns (e.g., results format, randomness details), error conditions, or any behavioral traits like side effects or limitations, leaving significant gaps.

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?

The description is a single, efficient sentence with no wasted words, making it appropriately concise. However, it's under-specified rather than optimally structured, as it could benefit from slightly more detail without losing efficiency.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a dice-rolling tool with 2 parameters, 0% schema coverage, no annotations, and no output schema, the description is incomplete. It doesn't explain the return values, parameter details, or behavioral context, making it inadequate for an agent to use effectively.

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 but adds minimal meaning. It mentions 'notation' without explaining what it is (e.g., dice notation like '2d6'), and doesn't address 'num_rolls' at all. This fails to compensate for the lack of schema documentation.

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' clearly states the action (roll) and resource (dice), but is vague about what 'notation' entails or how it differs from sibling tools like repair_cost and web_search. It doesn't specify the format or examples of dice notation, leaving the purpose somewhat ambiguous.

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 or in what context it's appropriate. The description lacks any mention of prerequisites, typical use cases, or comparisons to sibling tools, offering no help for an agent deciding when to invoke it.

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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