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Fresh Linkedin Profile Data MCP Server

Fresh Linkedin Profile Data MCP Server

by BACH-AI-Tools
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Get Open To Work Status

get_open_to_work_status

Check a LinkedIn profile's open-to-work status by providing the profile URL. Get a clear yes/no indicator for recruiting outreach.

Instructions

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": {
      "linkedin_url": "<linkedin_url>"
    },
    "name": "get_open_to_work_status"
  }
}

Input Schema

NameRequiredDescriptionDefault
linkedin_urlYesExample value: https://www.linkedin.com/in/williamhgates/

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

D1.3/5.0
Behavior1/5

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

No annotations are present, so the description carries the full burden of behavioral disclosure. It discloses nothing about side effects, data returned, access requirements, or limitations. The pricing note does not contribute to behavioral understanding.

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

Conciseness1/5

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

The single sentence is short but not appropriately sized because it omits essential functional information. It is not front-loaded with useful content; the pricing note is irrelevant to tool usage. No sentence earns its place in a functional description.

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

Completeness1/5

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

For a tool with one parameter and no output schema, the description is grossly incomplete. It fails to explain what the tool returns, how to interpret results, or any prerequisites. An agent has no idea what to expect or how to act on the response.

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 description coverage is 100% (the sole parameter linkedin_url is described with an example). Per rubric, high coverage gives a baseline of 3. The description adds nothing beyond the schema, but the schema is self-explanatory for a simple URL input.

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

Purpose1/5

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

The description is only '**1 credit per call.**' which is a pricing note and does not state what the tool does. The tool name hints at retrieving a LinkedIn user's open-to-work status, but the description itself is tautological (repeats nothing) and provides no verb or resource.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given on when to use this tool, what alternatives exist, or any exclusions. The description is entirely silent on usage context, leaving the agent to infer 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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