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

Annotations already declare readOnlyHint=false, destructiveHint=false, and openWorldHint=true, so the safety profile is covered. The description adds that generation is AI-driven and produces word-clue pairs plus an interactive grid, which hints at latency/cost, but it never states that generation is slow, may consume credits, or whether the puzzle is persisted or merely returned. Since 'creates' plus openWorldHint implies external processing, a 3 is appropriate — useful context, not rich disclosure.

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?

Three sentences, each doing distinct work: what it generates, the mode split with parameter grouping, and the mode summary. The generative action is front-loaded with no preamble or filler.

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 complex, open-world generative tool with 13 parameters and no output schema, the description covers inputs well and sketches the return ('word-clue pairs and an interactive grid puzzle'), but says nothing about generation time, failure modes for invalid URLs, or whether results persist server-side. An agent can call it, but cannot predict cost or post-call state.

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 100%, so baseline is 3, but the description adds genuine structural meaning: it groups the 13 parameters into two mutually exclusive modes and enumerates the exact source set (notes/webpage/youtube/documents/images/audio/ai_research/ai_chat_context) that belongs to the standalone mode. That mutual-exclusivity framing is the single most important thing an agent needs and is only weakly implied by the schema's '(optional - use this OR provide sources)'.

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 ('Generate an AI-powered crossword puzzle') and immediately splits it into two named input modes (unit_id vs. custom sources). The resource is distinct enough from siblings like create_quiz, create_flashcards, and create_mcq that an agent can pick it without opening the 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?

The 'either unit_id or custom sources' framing is real routing guidance, reinforced by 'Supports both course-based and standalone modes', which resolves the otherwise ambiguous 13-param, zero-required schema. It stops short of naming alternatives or exclusions — e.g. that edit_crossword should be used to modify an existing puzzle rather than creating a new one.

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