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BACH-AI-Tools

Li Data Scraper MCP Server

get_profiles_comments

Retrieve the last 50 comments from a LinkedIn profile to analyze engagement and track user interactions on the platform.

Instructions

Get last 50 comments of a profile. 1 credit per call

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions '1 credit per call', which adds useful context about cost or rate limits, but fails to describe other critical behaviors like response format, pagination (since it specifies 'last 50'), error handling, or authentication needs. This leaves significant gaps in understanding how the tool operates.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise and front-loaded, consisting of just two short sentences that directly state the tool's function and a key operational detail ('1 credit per call'). Every word earns its place, with no wasted information, making it highly efficient and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and a cost hint, but lacks details on output structure, error cases, or how 'last 50' is determined (e.g., sorting or time range). For a tool with no structured data to rely on, this leaves the agent with incomplete contextual understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0 parameters with 100% coverage, so the schema fully documents the absence of parameters. The description does not add parameter-specific information, which is appropriate here. Since there are no parameters, the baseline is 4, as the description does not need to compensate for any schema gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Get') and resource ('last 50 comments of a profile'), making the purpose specific and understandable. However, it does not explicitly distinguish this tool from sibling tools like 'get_profile_post_and_comments' or 'get_profile_post_comment', which might handle similar comment-related queries, so it misses full sibling differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives, such as sibling tools that might retrieve comments in different contexts (e.g., by post or with filtering). It mentions '1 credit per call', which hints at cost but does not define usage scenarios or exclusions, leaving the agent without clear contextual direction.

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

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