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

Beyond the annotations, it reveals login requirements, platform restrictions, WEEKLY/MONTHLY parameter coupling, date-as-day semantics in Beijing time, assigneeId must be an existing collaborator and notification behavior, and the meaning of the generated return value plus auto-fill on later reads. This is substantial context that annotations cannot 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 compact and densely organized: three labeled sections ('何时用', '组合链', '口径/坑') with bulleted pitfalls. Every sentence carries operational value and the most important routing and caveats are front-loaded.

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 9-parameter mutating tool with no output schema, it covers auth, platform limitations, required relationships, date/time semantics, frequency-specific constraints, response meaning (generated count), and future behavior. The lifecycle chain also tells the agent what to call next, which is more than sufficient context.

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 only 22%, so the description bears the burden of explaining parameters, and it does so for the non-obvious ones: weekday must accompany WEEKLY (1=Mon...7=Sun), monthday must accompany MONTHLY with overflow clamped to month end, assigneeId must be a target collaborator or omitted for self, and dates are day-level YYYY-MM-DD Beijing time. This adds real meaning beyond raw schema types.

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 concrete action and resource: build a recurring-task rule that automatically generates future instances for long-term cadences like '每周一对齐一次'. It distinguishes from lifecycle siblings by naming update_task_series and stop_task_series for later modification/termination, and clarifies that only web/pro can create, so it is not a generic task-creation endpoint.

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?

An explicit '何时用' section gives the trigger condition (long-term recurring cadence) and platform constraint (web/pro only, App read-only). It also gives a recommended chain: create → verify with get_collaboration_goal(include:['series']) → later update/stop. It does not explicitly state when to use a one-off task sibling (e.g., create_collaboration_task) instead, so the routing is not fully exhaustive.

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