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

Annotations already declare readOnlyHint=false and destructiveHint=false, so the write-but-non-destructive profile is covered structurally. The description adds one genuinely useful piece of context not in the annotations: the required send scope, which is a real auth prerequisite. It does not say whether feedback can be resubmitted, whether it overwrites prior feedback, or what the call returns.

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?

Two short sentences, purpose first and the scope prerequisite second, with no filler. The phrase 'to improve future generations' is slightly vague about the effect but occupies little space and is not 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 two-parameter, non-destructive write tool with full schema coverage and annotation coverage, the definition is adequate: it says what it does and what scope is needed. It stops short of describing the outcome of submission (whether feedback is immediately applied to training, stored for review, or acknowledged), which is the remaining gap for a tool whose whole purpose is a side effect.

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% (id as message ID, feedback as text with 1-2000 length bounds), so the schema carries parameter meaning. The description adds nothing about the id referring to an AI-generated message specifically versus any message, nor about what kind of feedback text is useful. 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?

The description names a specific verb and object: submitting feedback on an AI-generated message, with the stated aim of improving future generations. That is enough for an agent to separate it from message lifecycle siblings such as approve_message, reject_message, or retry_message, though it never explicitly names those siblings as distinct.

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?

It gives a rationale ('improve future generations') but no actual when-to-use or when-not-to-use guidance, and no comparison to plausible alternatives like approve_message or reject_message. An agent must infer that this is a post-generation quality signal rather than an approval action.

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