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rcreech93

LinkedIn MCP

by rcreech93

linkedin_profile

Extract structured LinkedIn profile data including experiences, education, skills, and interests from a profile URL or username.

Instructions

Extract a LinkedIn profile using web scraping with Selenium.

Args:
    profile: The LinkedIn profile URL or username
    extract_educations: If True, extract educations
    extract_skills: If True, extract skills
    extract_interests: If True, extract the profile interests

Returns:
    The LinkedIn profile as a dictionary

Example output in case of success:
    {
        "given_name": "Gil",
        "surname": "Fernandes",
        "email": "gil-palma-fernandes@linkedin.com",
        "cv": "A Software Engineer and data scientist with over 25 years of professional experience in different companies. I spent a good part of my professional career working in projects related to the aviation industry and the rest as developer in Talend and Java based projects.

In 2019 I commenced my learning on Artificial Intelligence and Deep Learning and related technologies and participated in several Kaggle competitions. I dedicate my weekends to my pet projects for UK charities which promote well-being and meditation.", "summary": "", "industry_name": "Software Engineer at Onepoint Consulting", "geo_location": "London Area, United Kingdom", "linkedin_profile_url": "https://www.linkedin.com/in/gil-palma-fernandes", "experiences": [ { "institution_name": "Onepoint Consulting · Full-time", "linkedin_url": "https://www.linkedin.com/company/309728/", "website": "", "industry": "", "type": "", "headquarters": "", ...

Example output in case of error:
{
    "error": "Error extracting profile: Error extracting profile"
}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
profileYes
extract_skillsNo
extract_interestsNo
extract_educationsNo
Behavior3/5

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

The description reveals it uses web scraping with Selenium, which implies network/browser behavior, and includes an error example showing possible failure. However, it does not disclose potential issues like login requirements, rate limiting, or blocking, so transparency is moderate.

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 well-structured with Args, Returns, and examples, making it scannable. The example output is lengthy but informative; while it could be trimmed, it mostly earns its place.

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?

With no annotations and no output schema, the description compensates by detailing the return format with a comprehensive example and an error case. It covers all parameters but omits guidance on alternatives and edge-case behavior, making it largely complete but not exhaustive.

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

Parameters5/5

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

The description provides plain-language explanations for all four parameters, directly mapping to schema properties. The example output illustrates the effect of extraction flags, which is critical given the 0% schema description 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 uses a specific verb 'Extract' and resource 'LinkedIn profile', clearly stating the tool's purpose. It distinguishes from sibling 'profile_search' by focusing on extracting a specific profile, not searching.

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 when a specific LinkedIn profile URL or username is available, as shown by the 'profile' parameter. However, it does not explicitly contrast with 'profile_search' or state when not to use this tool, leaving guidance only implied.

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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