spine_learn_from_corpus

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

Discloses non-modification of source files, batch error handling, and export to cache. With no annotations, this provides sufficient safety and behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Four sentences with no redundancy; front-loaded with purpose. Every sentence contributes essential information.

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

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

Covers purpose, process, safety, error handling, and output format. Missing explicit mention of default output knowledge directory, but schema provides that.

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

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

Schema covers 100% of parameters, so baseline is 3. Description adds no parameter-specific detail beyond the schema's descriptions.

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

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

Description clearly states the tool builds the local Spine Corpus Learning Layer and describes its scanning and extraction process. However, it does not explicitly differentiate from spine_scan_corpus or other siblings.

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

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

Implies use for corpus learning layer building and warns against live animation/UI automation, but lacks explicit when-to-use or alternative tool comparisons.

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