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linkedin-web-scrapper-mcp-server

linkedin-web-scrapper-mcp-server

by Phicks-debug
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Local

scrape-linkedin-profile

Scrape a LinkedIn profile URL to extract comprehensive data including experience, education, skills, licenses, certifications, and more.

Instructions

Scrape comprehensive data from a specific LinkedIn profile URL. Returns detailed profile information including experience, education, skills, licenses & certifications, and more.

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": {
      "profileUrl": "<profileUrl>"
    },
    "name": "scrape-linkedin-profile"
  }
}

Input Schema

NameRequiredDescriptionDefault
profileUrlYesThe LinkedIn profile URL to scrape (e.g., 'https://www.linkedin.com/in/username/')

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.6/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes what data is returned (experience, education, etc.) but does not disclose potential side effects, limitations (e.g., profile privacy, rate limits), or authentication requirements. For a scraping tool, such context is important but missing.

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 concise sentences clearly state the action and the output categories. No filler or unnecessary repetition. The most important information is front-loaded.

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 simple one-parameter tool with no output schema, the description adequately explains the return values by listing key sections. However, it lacks any caveats about scraping LinkedIn (e.g., profile must be public, potential blocks) which would be valuable for completeness, but the tool is simple enough that this is a minor gap.

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 schema covers the single parameter (profileUrl) 100%, including an example. The description adds no additional meaning beyond what the schema already provides. Baseline of 3 is appropriate given full schema coverage.

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 tool's specific action ('Scrape comprehensive data from a specific LinkedIn profile URL') and the resource. It distinguishes from the sibling tool 'search-linkedin-people' by emphasizing 'specific URL' versus search functionality.

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

Usage Guidelines3/5

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

The description implies usage: scrape when you have a specific LinkedIn profile URL. However, it does not explicitly state when not to use it or mention the alternative sibling tool (search-linkedin-people) for finding profiles. This is implied rather than explicitly guided.

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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