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BACH-AI-Tools

LinkedIn Data API MCP Server

get_company_jobs_count

Get the total number of open jobs a company has posted on LinkedIn. Input a company ID to receive the count, enabling analysis of hiring demand.

Instructions

Get total number of opening jobs the company

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
companyIdYesExample value: 1441
Behavior2/5

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

With no annotations provided, the description must fully disclose behavioral traits. It states 'Get total number' but does not mention whether the count is live, cached, or subject to rate limits. It also doesn't confirm that this is a read-only operation (though implied). No side effects or authorization requirements are described, leaving the agent partially in the dark.

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 a single sentence with no wasted words. It is concise and front-loaded with the action. However, it could be restructured for clarity (e.g., 'Get the total number of opening jobs for a company'). Still, it meets efficiency standards.

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 simplicity of the tool (one parameter, no output schema), the description should clarify the return format (e.g., integer count) and the meaning of 'opening jobs' (e.g., actively recruiting positions). It lacks these details, and with no output schema, the agent has no way to know what the response structure is. The description also fails to differentiate from similar count tools among siblings.

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

Parameters3/5

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

The input schema has 100% description coverage for the only parameter 'companyId', providing an example value. The description adds no additional meaning beyond that. Per guidelines, when schema coverage is high, a score of 3 is appropriate as baseline, and the description does not elevate it by adding context like parameter format or source.

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 uses a specific verb 'Get' and specifies the resource 'total number of opening jobs', making the core purpose clear. It also naturally distinguishes from sibling tools like 'get_company_jobs' (which likely lists jobs) and 'get_company_employees_count'. However, the phrasing is slightly awkward, missing 'for' before 'the company'.

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?

No guidance is provided on when to use this tool versus alternatives. For example, it doesn't explain that this tool returns a count while 'get_company_jobs' returns a list, nor does it suggest when one is preferred over the other. There is no context on prerequisites or limitations.

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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