import_learnings

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

The description discloses deduplication against existing learnings and parsing behavior, but with no annotations provided, it fails to cover authentication needs, error handling, or potential side effects like overwriting.

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 concise with 4 sentences that front-load the main action. It could be more scannable with bullets, but no redundant information is present.

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?

The description lacks information about the return value (e.g., success message, import summary) and error scenarios. For a tool with no output schema, this omission leaves the agent uncertain about what to expect after 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 coverage is 100%, so the schema already documents parameters. The description adds context like absolute path for file_path and default value for default_category, but does not significantly enhance understanding beyond the schema.

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's purpose as bulk-importing learnings from Markdown or JSON files, specifying parsing strategies for three formats. It effectively distinguishes from siblings like save_learning (single) and list_learnings (read).

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 provides clear context for when to use the tool (bulk import) but does not explicitly state when not to use it or mention alternatives like save_learning for single imports.

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