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1425sd

spine-mcp

by 1425sd

spine_get_generation_guide

Load pre-learned Spine generation guide and animation presets for informed parameter decisions. Run spine_learn_from_corpus if files are missing.

Instructions

Use this to load the learned Spine generation guide and machine-readable presets before deciding animation parameters. It reads learned-spine-guide.md, learned-animation-presets.json, and learned-naming-rules.json. If files are missing, run spine_learn_from_corpus first. Do not use it to scan corpus, call Spine CLI, or build animations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
knowledgeDirNoDirectory containing learned Spine knowledge files. Defaults to project knowledge/.
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses that the tool reads specific files and that missing files will cause failure, with a remediation suggestion. It implies read-only behavior but does not explicitly state the absence of side effects or performance implications. This is good but not exhaustive.

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 three sentences, each serving a distinct purpose: stating the action and context, listing the files, and providing error handling and exclusions. It is front-loaded with the primary use case, with no wasted words.

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 simplicity (one optional parameter, no output schema), the description covers all necessary information: purpose, usage context, prerequisite actions, and excluded use cases. It is complete for an agent to correctly select and invoke the tool.

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 one optional parameter 'knowledgeDir' with a description that matches the tool's purpose. Schema coverage is 100%, so the parameter is fully documented. The description does not add significant new information beyond the schema, as it already states the default location. Baseline 3 is appropriate.

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 verb 'load' and the specific resources (Spine generation guide, machine-readable presets, learned-spine-guide.md, learned-animation-presets.json, learned-naming-rules.json). It also explicitly distinguishes from sibling tools by stating what not to use it for (scan corpus, call Spine CLI, build animations).

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 (before deciding animation parameters) and what to do if files are missing (run spine_learn_from_corpus first). It also lists alternatives that are not appropriate, making the usage context very clear.

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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