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LinkedIn Api8 MCP Server

LinkedIn Api8 MCP Server

by BACH-AI-Tools
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_company_jobs_count

Retrieve the total number of open job positions for a company using its LinkedIn company ID. Get job count data to analyze hiring activity.

Instructions

Get total number of opening jobs the company

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "companyId": "<companyId>"
    },
    "name": "get_company_jobs_count"
  }
}

Input Schema

NameRequiredDescriptionDefault
companyIdYesExample value: 1441

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.5/5.0
Behavior3/5

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

Without annotations, the description carries the full burden. It clarifies it returns a count of 'opening jobs', but does not mention any behavioral traits such as rate limits, permissions, or response format. For a simple read operation, this is adequate but lacks depth.

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 a single, direct sentence with no filler or redundancy. Every word earns its place.

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 (one parameter, no output schema) and the presence of many sibling tools, the description is minimally complete but could mention the return type (e.g., integer) to fully inform the agent.

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?

Schema coverage is 100% with the parameter 'companyId' described (example value). The description adds no extra parameter semantics beyond the schema, so the baseline score of 3 applies.

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

Purpose5/5

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

The description clearly states the verb 'Get' and the resource 'total number of opening jobs', distinguishing it from sibling tools like 'get_company_jobs' (which likely returns a list) and 'get_company_employees_count'.

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, nor any context about prerequisites or exclusion criteria. The agent must infer use cases from the name alone.

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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