capture_learning_signal

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

The description reveals the append-only nature of the storage, which contradicts the idempotentHint annotation (true). While it adds context about learning after 30+ signals, the contradiction undermines trust in the tool's behavior.

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 three sentences that front-load the core purpose. No fluff or redundant information; every sentence adds value.

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 covers purpose, usage timing, storage location, and learning outcome. For a recording tool with 9 parameters and no output schema, this is fairly complete, though it omits details on duplicate signals or side effects.

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?

With 78% schema description coverage, the input schema already documents most parameters. The description does not add significant new semantic meaning beyond what the schema provides, such as detailed usage tips for specific parameters.

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 verb 'Record' and the resource 'outcome of an agent action or skill invocation' with a specific purpose for continuous learning. It distinguishes itself from siblings like get_learning_signals and store_trade_lesson by focusing on recording actions for pattern emergence.

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 advises using the tool after any significant agent action, providing a general usage context. However, it does not explicitly state when not to use it or compare it to alternative tools like get_learning_signals for retrieval or store_trade_lesson for trade-specific lessons.

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