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

The description goes well beyond the readOnly/idempotent annotations by disclosing the once-a-day scrape, possible missed comments, history start tied to the ContentIn account, and the absence of reactor identities. It even gives explicit instructions on what not to claim, which is essential to prevent hallucinated answers.

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 main purpose and use cases are front-loaded, and the important caveats are organized into labeled sections. Though lengthy, every sentence earns its place by preventing incorrect claims about data freshness and engagement identity.

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 read-only list tool with six parameters and no output schema, the description covers response contents, pagination expectations, data freshness limits, and critical false-claim pitfalls. An agent has enough context to call it correctly and interpret results safely.

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?

The schema already covers all six parameters at 100%, so the baseline is 3. The description adds valuable operational guidance by emphasizing that thousands of rows are normal and that pagination via before/before_id should be used instead of fetching everything, which goes beyond the schema's raw descriptions.

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 ('List'), a precise resource ('comments this user has left on OTHER people's LinkedIn posts'), the sort order ('newest first'), and the output essentials ('comment urn and a direct link'). This makes the tool clearly distinguishable from siblings like list_posts and get_post_analytics.

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 provides explicit use cases: review commenting activity, find a conversation, or hand identifiers to another tool. It also advises paging rather than requesting everything, but it does not explicitly name alternatives or state when not to use this tool, so it stops short of a 5.

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