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

Beyond the annotations (readOnlyHint=false, destructiveHint=false, etc.), the description discloses crucial runtime behaviors: re-preparing the same seat/reservation auto-supersedes the pending action, distinct preparations queue independently and cause confirm_action to refuse execution, and the reserve flow is asynchronous while cancel/swap are synchronous. It also states the mcp_session_id/LIBRARY login requirement. These traits are not derivable from the annotations and materially affect how an agent should behave.

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?

Every sentence earns its place: core purpose, single-action default, supersede/multi-action edge cases, how to specify an action, async vs direct sibling flows, and auth requirement. The information is front-loaded with the main verb first and follows a logical order from simple to complex cases. No redundancy or filler, despite the length being justified by the tool's genuinely complex semantics.

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 high complexity (superseding, multi-action ambiguity, async reservation flow) and the absence of an output schema, the description covers the behavioral contract very well. However, it does not describe what a successful execution returns (acknowledgment, status, error shape) or the edge case of no pending action at all. These are minor gaps for an executing tool, but an output schema or one more sentence on return values would make it complete.

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 the baseline is 3. The description adds workflow-level meaning beyond the schema: it explains the multi-pending-action case where action_id must be chosen from a returned list, and it ties action_id to the prepare-response actionId with context on superseding. It also clarifies that mcp_session_id is needed for LIBRARY login while the schema already covers the safe fallback binding. Genuinely useful, though the schema already carried much of the per-parameter meaning.

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 states a specific verb+resource: it '최종 확인하고 실행합니다' (finalizes and executes) user-approval pending actions prepared on SSU Campus. It clearly differentiates from siblings by naming the prepare_* tools (which create the pending actions), get_library_wait_status (which checks async results), and cancel_library_wait, so an agent can tell exactly which stage of the workflow this tool occupies.

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 gives explicit when-to-use rules: call without action_id when exactly one action is pending; do not call expecting execution when multiple distinct actions are pending (it returns a list of action_ids instead); pass the prepare response's actionId to target a specific action. It also routes the agent correctly across the workflow, noting that prepare_reserve_library_seat is async and requires get_library_wait_status for final results, while prepare_cancel/swap execute directly. This is unambiguous operational guidance.

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