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

Annotations already declare readOnlyHint=false, openWorldHint=true and destructiveHint=false, so the agent knows this is an external, non-destructive write. The description adds generation-specific behavior (AI-authored content, 17 layouts, 12 styles, dual mode) but says nothing about latency, cost, whether the artifact is persisted, or how the result is returned — notable for an AI generation call with no output schema.

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, front-loaded with the core verb and the mode split. It wastes some space re-enumerating layout and style examples that the enum already lists in full, which is the only real padding.

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 16-parameter generative tool with no output schema, the description should say more about the result: whether an infographic is rendered inline, saved as an artifact needing an ID, or returned as a URL, and whether generation is long-running. Mode selection and source families are well covered, but the post-call story is still missing.

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 earns more by grouping the eight source parameters into one conceptual family ('notes/webpage/youtube/documents/images/audio/ai_research/ai_chat_context') and reinforcing that title is required in source mode. It also conveys that unit_id produces a preview of the next 3 syllabus sections, adding intent beyond the schema's field-level text.

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 educational infographic') and immediately splits the tool into its two operating modes (unit_id vs. custom sources). It never names edit_infographic to disambiguate create-vs-edit, but the creation framing is unambiguous enough that an agent will not confuse the two.

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?

Explicitly tells the agent which mode to pick: unit_id 'for learning roadmap' (course-based) vs. sources 'for any topic' (standalone). It does not state when NOT to use this tool, nor does it route the agent to edit_infographic for modifying an existing infographic.

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