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DangerBlack

Fantasy World MCP Simulator

by DangerBlack

loadWorld

Restore a previously saved fantasy world from JSON data to resume simulation with its complete state.

Instructions

Load a world from previously saved JSON data. Use this when resuming a world from AI context.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
worldDataYesFull JSON data of the world (copy from previous getWorldState or exportWorld result)
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. The description does not mention that loading likely overwrites the current world state, whether the JSON is validated, or what happens after loading (e.g., confirmation or errors). It only states the action without side effects, which is a significant gap for a state-changing tool.

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 only two sentences, front-loaded with the primary action, and includes a practical usage tip. Every word is purposeful, with no redundant information. This is a model of conciseness.

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?

The tool is simple with only one parameter and a well-described schema. However, the lack of annotations and an output schema means the description must compensate. It covers purpose and usage but omits behavioral effects like overwriting the current context or error handling. Overall, it is minimally complete but with clear gaps in side-effect disclosure.

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 schema description provides 100% coverage, describing the parameter as 'Full JSON data of the world (copy from previous getWorldState or exportWorld result).' This gives clear guidance on what to pass and where to get it. Since the schema already handles the semantics, the description does not need to add more, earning the baseline score of 3.

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

Purpose5/5

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

The description clearly states the tool's function: 'Load a world from previously saved JSON data.' The verb 'load' and resource 'world' are specific, and the source (previously saved JSON data) distinguishes it from sibling tools like initializeWorld (create new) and getWorldState (read current state). The additional context 'resuming a world from AI context' further clarifies its purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use the tool: 'Use this when resuming a world from AI context.' This provides a clear context for usage. However, it does not explicitly mention alternatives or when not to use it, though sibling tools imply alternatives. The missing exclusions prevent a score of 5.

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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