dashclaw_learning_log

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

No annotations exist, so the description must convey behavior. It states it logs (write) and that outcome can be updated later, but does not disclose idempotency, side effects, authentication needs, or success/failure indicators. Basic but incomplete.

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 two sentences: the first states the action and object, the second provides usage guidance. No fluff, all information is essential and front-loaded.

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?

For a tool with 4 parameters, 1 required, no output schema, and no annotations, the description adequately explains purpose and when to use. However, it lacks details on return values, error cases, or how to later retrieve logged data, which would be helpful for an agent.

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 75% (3 of 4 parameters have descriptions). The description adds value by tying parameters to purpose ('reasoning and outcome'), reinforcing that context and outcome are part of the log. However, agent_id lacks schema description, and the description does not clarify its role, so some gap remains.

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 logs a decision and outcome to the learning database, with the explicit purpose of recalling reasoning in future sessions. This distinguishes it from sibling tools like dashclaw_learning_query (query) and dashclaw_recent_decisions (list).

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 a clear usage context: 'Use after making a non-obvious decision so future sessions can recall the reasoning and outcome.' It does not explicitly exclude alternatives but implies this is for logging, contrasting with query siblings.

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