Fresh Linkedin Profile Data MCP Server

Count

count

Find out how many job openings a company has on LinkedIn. Provide the company ID to get the total number of posted positions.

Instructions

Get the number of job openings a company has posted on LinkedIn. 1 credit per call.

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": {
      "company_id": "<company_id>"
    },
    "name": "count"
  }
}

Input Schema

Name	Required	Description	Default
`company_id`	Yes	Example value: 162479

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.0.0Sep 4, 2026

TDQS

B3.4/5.0

Behavior3/5

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It does add a useful cost note ('1 credit per call'), but it does not mention the return shape, error behavior, or whether the count reflects only currently open roles vs. historical postings. The credit information lifts it above a bare statement, but gaps remain.

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?

Two short sentences convey the core purpose up front and add the cost detail without any filler or redundant phrasing.

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

Completeness4/5

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

For a one-parameter, read-only count tool with no output schema, the description provides sufficient context for basic selection: what the tool does and what it costs. However, it omits the response format and any edge-case behavior, which would be helpful given the absence of annotations.

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%, but the only parameter description is an example value ('162479'), which conveys no semantic meaning. The tool description implicitly clarifies that company_id identifies the company whose openings are counted, but it does not explicitly define the parameter's format or valid values.

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 names a specific verb and resource: getting the number of job openings a company has posted on LinkedIn. It is clear and easily distinguished from sibling tools, though it does not explicitly contrast itself with alternatives like search_jobs or get_job_details.

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?

There is no guidance about when to use this tool versus alternatives, nor any exclusions or prerequisite conditions. The only usage signal is implied by the purpose statement itself.

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

Deploy Server