scrape_post_comments

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

With no annotations, the description carries the full behavioral burden. It discloses the persistent 'store' side effect, a hard cap at 200 comments, nested reply behavior, and per-profile costs for profile_mode='main'. It does not mention auth requirements, rate limits, or duplicate handling, but the major behavioral traits are surfaced.

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 front-loaded with the core purpose, followed by a short motivation sentence, then a structured Args block. Every sentence earns its place and the parameter documentation is tight and readable with 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?

Given that an output schema exists and all parameters are well documented, the agent has enough to invoke the tool correctly. Missing context is mainly cross-tool: it does not clarify when to use list_comments instead, nor does it note any prerequisite such as Apify credentials or LinkedIn access. These are relevant but not fatal 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 0%, so the description must fully compensate. It does: post_url documents both accepted URL formats, max_items documents default and cap, include_replies explains nested replies, and profile_mode explains cost and detail tradeoff. Every parameter receives meaning beyond the raw schema.

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 first sentence names a specific operation and target: 'Scrape and store comments on a LinkedIn post via Apify.' It is clear that this is an external scraping/writing tool, not the same as list_comments. However, it does not explicitly contrast itself with sibling list_comments, so it stops short of top-tier sibling differentiation.

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 description gives a concrete use case: 'Use this to research who is engaging with a specific post' and lists the insight types it supports (warm leads, decision-makers, competitor mentions, pain points). This is clear context for when to invoke it, but it omits explicit 'when not to use this' guidance or alternatives such as list_comments for already-stored data.

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