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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description adds value by detailing the output contents (per-component values, reasons, alternatives, etc.). It does not contradict annotations, and it provides behavioral context beyond what annotations convey.

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 long, front-loaded with the key outputs, and ends with a usage recommendation. Every sentence is necessary and well-structured.

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 tool has an output schema (not shown but implied), annotations, and 75% schema coverage. The description explains the return value in detail and states its purpose. It could mention prerequisites or that context is optional, but overall it is fairly complete for a debugging tool.

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 75% (3 of 4 parameters described). The description mentions 'in a given mood/context' which adds context for the context parameter, but does not fully detail its structure. For parameters with schema descriptions, the description adds little beyond what the schema provides.

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 returns a full score breakdown for a specific program in a given mood/context, listing specific outputs. It distinguishes itself from sibling tools like tv_recommend_by_mood by focusing on explaining an existing recommendation rather than generating one.

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 explicitly says 'Use to understand or debug a recommendation,' providing clear guidance on when to use it. It doesn't explicitly state when not to use it, but the context and sibling tool names imply alternatives for other tasks.

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