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BACH-AI-Tools

Li Data Scraper MCP Server

get_companys_post

Retrieve the latest 50 posts from any company on LinkedIn using the Li Data Scraper MCP Server to monitor business activity and content.

Instructions

Get last 50 posts of a company. 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', indicating a cost or rate limit, which is useful. However, it lacks details on permissions, data freshness, error handling, or return format, leaving significant gaps for a tool that fetches data.

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

Conciseness4/5

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

The description is very concise with two short sentences that convey key information: the action and a cost note. It's front-loaded and wastes no words, though it could be slightly more structured by explicitly stating the tool's scope or limitations.

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

Completeness2/5

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

Given the tool's complexity (fetching company posts) and lack of annotations and output schema, the description is incomplete. It doesn't explain what 'posts' entail, the data format returned, or any prerequisites, making it inadequate for an agent to use effectively without additional context.

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 no parameter documentation is needed. The description doesn't add parameter details, but this is acceptable given the schema's completeness. A baseline of 4 is applied as it adequately handles the lack of parameters without introducing confusion.

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 action ('Get') and resource ('last 50 posts of a company'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_profiles_posts' or 'get_profile_post_and_comments', which might have overlapping functionality, so it doesn't reach the highest score.

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 'get_profiles_posts' or 'search_post_by_keyword'. It mentions '1 credit per call', which hints at cost but doesn't inform usage context or exclusions, leaving the agent without clear direction on tool selection.

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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