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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the tool is safe and read-only. The description adds valuable behavioral context: the auto-fallback from strict to average fairness, the degraded flag, per-person score breakdown, and compromise note. This goes beyond what annotations provide.

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, front-loaded with the core purpose, and every part adds value. No redundant or wasted words. The structure efficiently conveys the algorithm and output features.

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?

Given the tool's complexity (7 params, nested objects, output schema), the description explains the core scoring logic and output enrichment (degraded flag, breakdown, compromise). However, it omits details about parameters like limit, min_score, timeframe, and include_streaming_xref. The output schema likely covers return values, so the description is mostly complete but has minor gaps.

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 only 29%, meaning most parameters (like limit, min_score, timeframe, include_streaming_xref) are not described in the schema. The description does not explain these parameters; it only indirectly mentions fairness. For a low-coverage schema, the description should compensate by detailing parameters, but it fails to do so.

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 it finds TV programs satisfying two people with different moods/preferences, with specific fairness logic. It distinguishes from sibling tools like tv_recommend_by_mood (single user) or tv_recommend_today (general), making the tool's unique role evident.

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 explains the default fairness mechanism and the auto-fallback behavior, providing clear context for when to use this tool. However, it does not explicitly state when not to use it or mention alternatives like tv_recommend_by_mood for single users, which could further guide selection.

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