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AI Makerspace MCP Demo Server

by lalrow

roll_dice

Generate random dice rolls using standard dice notation for games, simulations, or decision-making scenarios.

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:19-23 (handler)
    The primary handler for the 'roll_dice' MCP tool. Registered via @mcp.tool() decorator. It instantiates DiceRoller with inputs 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)
  • The __str__ method of DiceRoller class, which generates the formatted output string for single or multiple rolls by calling roll_dice or roll_multiple.
    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)
  • Core helper method in DiceRoller that parses dice notation (e.g., 2d20k1), rolls the dice using random.randint, sorts descending, and returns all rolls and kept rolls.
    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
  • Helper method to perform multiple rolls when num_rolls > 1, aggregating results with totals.
    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
  • server.py:19-19 (registration)
    The @mcp.tool() decorator registers the roll_dice function as an MCP tool.
    @mcp.tool()
Behavior2/5

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

No annotations are provided, so the description carries full burden. It states the basic action but lacks behavioral details such as how results are returned (e.g., sum, individual rolls), error handling for invalid notation, or any rate limits. This leaves significant gaps for a tool with parameters.

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 zero waste. It's front-loaded with the core action and resource, making it easy to parse quickly.

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 2 parameters with 0% schema coverage and no annotations, the description is incomplete—it doesn't explain parameter usage or behavioral traits. However, an output schema exists, so return values needn't be described, partially mitigating the gap. This is minimally adequate but with clear deficiencies.

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 'notation' but doesn't explain what dice notation entails (e.g., '2d6+1'), and 'num_rolls' is not addressed at all. This fails to add meaningful semantics beyond the bare parameter names.

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 clearly states the action ('Roll') and the resource ('dice'), specifying it operates with 'given notation'. It distinguishes from sibling tools that provide facts or web searches, though it doesn't explicitly differentiate from potential dice-rolling alternatives (none exist in the sibling list).

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 implies usage for dice rolling but doesn't specify contexts (e.g., games, simulations) or exclusions, and sibling tools are unrelated, so no comparative advice is given.

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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