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

Annotations already cover the safety profile (readOnlyHint=true), so credit goes to what the description adds: result ordering (newest first), a hard result cap of 2000, and an optional status filter. That cap is the most operationally important detail here, and it is disclosed concisely.

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?

One sentence plus one endpoint reference, both front-loaded with the behavior (ordering, cap, filter) ahead of implementation detail. No 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 two-parameter read tool with full schema coverage and no output schema, the description covers ordering, truncation, and filtering, which is the meaningful behavioral surface. It does not explain how to page past the 2000 cap or what fields each task carries, though the latter is largely implied by the sibling gladly_get_task.

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 100% and the status enum is fully enumerated in the schema, so the description's 'optionally only OPEN or CLOSED' restates known information. Baseline 3 is appropriate; the description adds nothing new about customer_id format or id resolution.

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 with an explicit scope constraint ('tasks of one customer'), which cleanly separates it from gladly_get_task (single task), gladly_list_customer_conversations, and the other list_* siblings. An agent can route to 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 customer-scoped framing implies when the tool applies, and the status option hints at filtering, but it never states when to prefer gladly_get_task for a single task or what to do when a customer has more than 2000 tasks. Usage is inferable rather than declared.

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