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BACH-AI-Tools

Li Data Scraper MCP Server

get_company_by_domain

Retrieve company information using a domain name to enrich business data through LinkedIn's data scraping capabilities.

Instructions

Enrich company data by domain. 1 credit per successful request.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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 adds some context: the credit cost implies a rate limit or billing consideration, and 'enrich' suggests it fetches external data. However, it lacks details on permissions, error handling, response format, or whether it's read-only or mutative, leaving significant gaps for a tool with no annotation coverage.

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: a single sentence that states the purpose and includes a critical behavioral note (credit cost). Every word earns its place, with no redundancy or unnecessary elaboration.

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 complexity (0 parameters, no output schema, no annotations), the description is minimally adequate. It covers the core purpose and a cost implication, but lacks details on return values, error cases, or integration context. For a data enrichment tool, more information on what 'enrich' entails would be helpful, though the absence of an output schema increases the burden.

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 implies the domain is the key input ('by domain'), but since there are no parameters, this is acceptable. Baseline is 4 for 0 parameters, as the description doesn't 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 tool's purpose: 'Enrich company data by domain' specifies both the verb ('enrich') and resource ('company data'), with the domain serving as the key input. However, it doesn't explicitly differentiate from siblings like 'get_company_details' or 'get_public_profile_data_by_url', which might offer overlapping functionality.

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. While it mentions '1 credit per successful request,' this is a cost implication rather than usage context. There's no mention of prerequisites, when-not-to-use scenarios, or comparisons to sibling tools like 'get_company_details' or 'search_people'.

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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