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

Annotations already declare the safety profile (readOnlyHint=false, idempotentHint=false, destructiveHint=false, openWorldHint=true), so the bar is lower. The description adds nothing beyond: it doesn't disclose whether followers are notified, how private vs public works, or side effects on the change timeline.

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?

A single short sentence with no waste and the action front-loaded. It is efficient, though the extreme sparseness means there is little structure to speak of.

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?

An output schema exists, so return values need not be explained, and params are fully covered by the schema. But for a mutating tool in a crowded sibling family, the absence of any usage or behavioral context leaves real gaps.

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%, so the schema already documents changeId, content, and the is_private enum meaning (1=private, 0=public). The description adds no parameter meaning beyond the schema, so the baseline 3 applies.

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 clear verb+resource: 'Add a comment/followup to a change.' An agent understands the action and target. However, it does not distinguish this from closely-named siblings like glpi_add_followup (generic) or glpi_add_change_solution, so the differentiation is missing.

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?

No when-to-use guidance, no exclusions, and no mention of alternatives. With three near-identical followup tools (change, problem, generic) plus change solutions, the description leaves the agent to guess which one applies.

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