import_learnings

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

With no annotations provided, the description carries the full burden, and it delivers: it details exactly what counts as marked, what permissive=true adds, and states deduplication. It does not mention side effects, errors, or write confirmation, but the import behavior is well specified.

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?

The description is dense but every sentence adds relevant information: file types, default marked behavior, permissive mode, and deduplication. It is front-loaded with the core purpose and avoids filler.

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?

Given the tool's moderate complexity, the description covers the core import rules, deduplication, and parameter behavior. It does not describe the return value or error handling, but no output schema exists and the provided detail is sufficient for correct invocation.

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 description coverage is 100%, so all parameters are already documented in the schema. The description reinforces the permissive parameter's meaning and adds detail about rule extraction, but it does not materially extend the meaning of project or default_category beyond their schema 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?

The description clearly states the tool bulk-imports learnings from Markdown or JSON files, with specific verb and resource. It is easily distinguished from the sibling save_learning, which implies single-item saving.

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?

The description explains the default marked-only behavior and when to set permissive=true, giving clear context for typical use. It does not explicitly mention alternatives like save_learning, but 'bulk-import' communicates the batch use case well enough.

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