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Txpple

fvtt-mcp-molten5e

by Txpple

level-up-pc

Add a level to a character, applying class or multiclass advancement. Supports dry-run for choices and rollback if advancement fails.

Instructions

Add ONE level to an existing PC (type:character) and apply that level's advancement IN PLACE. Same className as a class the PC already has → a single-class level-up; a class it does NOT have → a MULTICLASS add (the PC gets the 2024 multiclass proficiency SUBSET, not the full first-level kit). HP/features/subclass(@ the class's level 3)/spell-slots scale; @scale stays native. Like create-pc: call with no/partial choices to get a needsChoices[] dry-run (e.g. the subclass options at level 3 — the actor is NOT touched); fill choices (level → advancement-id → {chosen|selected|uuid}) and re-call. ASI ability bumps are NOT applied here — raise the final scores with update-actor; a feat taken at an ASI tier is added with add-feature. If a required advancement FAILS to apply, the PC is rolled back to its prior level and success:false is returned with errors[]. Required: actorIdentifier, className. Returns {success, actor (incl. classLevel + classes[]), applied[], needsChoices[], unresolvedScale[], errors[], warnings[]}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hpModeNoavg
choicesNo
classNameYes
acceptDefaultsNo
actorIdentifierYes
Behavior5/5

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

Despite no annotations, the description thoroughly discloses behavioral traits: in-place application, rollback on failure, dry-run mode that doesn't modify the actor, multiclass proficiency subset, HP/feature/spell-slot scaling, and the return structure. No contradictions.

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 efficient: every sentence adds value, starting with the core action, then nuances, then alternatives and error handling. It is well-structured and not verbose.

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

Completeness5/5

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

Given the tool's complexity, no output schema, and no annotations, the description is remarkably complete. It covers all key aspects: input parameters, behavior, dry-run, rollback, return values, and cross-references to sibling tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema coverage, the description adds significant meaning for most parameters: className behavior, actorIdentifier requirement, choices structure detailed as level->advancement-id->{chosen,selected,uuid}. However, acceptDefaults and hpMode are not explicitly mentioned, relying on inference. Still, it compensates well overall.

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 it adds one level to an existing PC, distinguishing between single-class and multiclass levels. It also explicitly distinguishes from sibling tools like create-pc, update-actor, and add-feature by specifying their roles.

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use this tool versus alternatives: ASI bumps must be done via update-actor, feats via add-feature. It also explains the dry-run pattern and rollback behavior, giving clear context for use.

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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