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D&D MCP Server

calculate_experience

Calculate experience point distribution for D&D encounters based on party size, average level, and total encounter XP to determine fair rewards.

Instructions

Calculate experience points for an encounter.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
party_sizeYesNumber of party members
party_levelYesAverage party level
encounter_xpYesTotal encounter XP value

Implementation Reference

  • The main handler function for the 'calculate_experience' tool. It calculates adjusted XP using party size multipliers from D&D 5e rules and divides by party size for XP per player.
    def calculate_experience(
        party_size: Annotated[int, Field(description="Number of party members", ge=1)],
        party_level: Annotated[int, Field(description="Average party level", ge=1, le=20)],
        encounter_xp: Annotated[int, Field(description="Total encounter XP value", ge=0)],
    ) -> str:
        """Calculate experience points for an encounter."""
        # D&D 5e encounter multipliers based on party size
        if party_size < 3:
            multiplier = 1.5
        elif party_size > 5:
            multiplier = 0.5
        else:
            multiplier = 1.0
    
        adjusted_xp = int(encounter_xp * multiplier)
        xp_per_player = adjusted_xp // party_size
    
        return f"""**Experience Calculation:**
    Base Encounter XP: {encounter_xp}
    Party Size Multiplier: {multiplier}x
    Adjusted XP: {adjusted_xp}
    **XP per Player: {xp_per_player}**"""
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 of behavioral disclosure. It states the tool calculates experience points, implying a read-only computation, but doesn't disclose if it modifies game state (e.g., updates character XP), requires specific permissions, has rate limits, or what the output format might be. For a tool with no annotations, this is a significant gap in transparency.

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: 'Calculate experience points for an encounter.' It's front-loaded with the core purpose, has zero wasted words, and is appropriately sized for a straightforward calculation tool. Every word earns its place.

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 tool's complexity (a calculation with three parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain the calculation logic, output format, or behavioral implications (e.g., whether it's idempotent or has side effects). For a tool with no structured output documentation, the description should provide more context to be fully helpful.

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?

The input schema has 100% description coverage, with clear parameter names and descriptions (e.g., 'Number of party members' for party_size). The description adds no additional meaning beyond the schema, such as explaining how these parameters interact in the calculation or providing examples. Given the high schema coverage, a baseline score of 3 is appropriate.

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 tool's purpose: 'Calculate experience points for an encounter.' It specifies the verb ('calculate') and resource ('experience points'), and the context ('for an encounter') distinguishes it from sibling tools like 'roll_dice' or 'update_character'. However, it doesn't explicitly differentiate from potential similar tools (e.g., if there were a 'calculate_damage' tool), so it's not a perfect 5.

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 prerequisites (e.g., requires an active encounter), exclusions (e.g., not for non-combat events), or related tools (e.g., 'end_combat' might be a follow-up). This leaves the agent to infer usage from the name and parameters alone.

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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