read_my_mood

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

With no annotations, the description carries the full burden and does well: it discloses the epistemic limits of the tool ('mood inference is often wrong'), instructs that the result be presented as correctable, and names the concrete signals used. It omits anything about latency, caching, or how much history is scanned, but the fallibility disclosure is unusually valuable 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?

Front-loaded with the core purpose, then the return shape, then the interaction rule. The quoted example earns its space by encoding tone, though the enumeration of return fields partially duplicates the output schema and could be trimmed.

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 zero-parameter tool with a rich output schema and no annotations, this covers purpose, output framing, and the key caveat about unreliable inference. The only real gap is not routing the agent to the mood-recommendation siblings when the user actually wants a suggestion.

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?

The tool takes zero parameters, so the baseline is 4. There is nothing for the description to disambiguate, and it correctly avoids inventing input semantics.

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?

States a specific verb and resource — infer the listener's current mood from recent listening — and frames it as an evidence-backed read, which cleanly separates it from the recommendation siblings (recommend_for_mood, recommend_from_playlist_for_mood). An agent can tell what this returns without opening the output schema.

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?

It gives clear interaction guidance (lead with evidence, offer the inference as a correctable read) but never says when to call this instead of, or before, recommend_for_mood. The when-to-use case is only implied by the conversational framing.

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