Skip to main content
Glama
anilsharmay

Country Explorer MCP Server

by anilsharmay

roll_dice

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

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:20-24 (handler)
    The main handler function for the 'roll_dice' tool. It is registered via @mcp.tool() decorator, creates a DiceRoller instance with the provided notation and number of rolls, 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 DiceRoller class implements the core dice rolling logic, including parsing notation like '2d20k1', rolling random dice, keeping the highest rolls, handling multiple rolls, and formatting the output string used by the 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)
  • server.py:20-20 (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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action (roll dice) but doesn't describe what the tool returns, how it handles errors, or any constraints (e.g., valid notation formats, limits on num_rolls). This leaves significant gaps in understanding the tool's behavior.

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, 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 the tool's low complexity and the presence of an output schema (which handles return values), the description is somewhat complete but lacks details on parameter semantics and behavioral traits. It covers the basic purpose but doesn't fully compensate for the absence of annotations and low schema coverage.

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 for undocumented parameters. It mentions 'given notation' for the 'notation' parameter but doesn't explain what dice notation entails (e.g., '2d6' for two six-sided dice). The 'num_rolls' parameter isn't addressed at all. This adds minimal value beyond the schema.

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 (unsplash_search, web_search), which is reasonable since they're unrelated, but the purpose remains somewhat ambiguous without explaining dice notation.

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. It doesn't mention any context, prerequisites, or exclusions, leaving the agent to infer usage based on the tool name alone. This lack of explicit or implied guidance reduces its effectiveness.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/anilsharmay/mcp-demo'

If you have feedback or need assistance with the MCP directory API, please join our Discord server