Skip to main content
Glama

get_person_profile

Retrieve structured LinkedIn profile data by entering a username to access professional information and connections.

Instructions

Get a specific person's LinkedIn profile.

Args: linkedin_username (str): LinkedIn username (e.g., "stickerdaniel", "anistji")

Returns: Dict[str, Any]: Structured data from the person's profile

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
linkedin_usernameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The core handler function for the 'get_person_profile' tool. It constructs the LinkedIn URL, scrapes the profile using linkedin_scraper.Person, structures experiences, educations, interests, accomplishments, and contacts into dictionaries, and returns the profile data or handles errors.
    @mcp.tool()
    async def get_person_profile(linkedin_username: str) -> Dict[str, Any]:
        """
        Get a specific person's LinkedIn profile.
    
        Args:
            linkedin_username (str): LinkedIn username (e.g., "stickerdaniel", "anistji")
    
        Returns:
            Dict[str, Any]: Structured data from the person's profile
        """
        try:
            # Construct clean LinkedIn URL from username
            linkedin_url = f"https://www.linkedin.com/in/{linkedin_username}/"
    
            driver = safe_get_driver()
    
            logger.info(f"Scraping profile: {linkedin_url}")
            person = Person(linkedin_url, driver=driver, close_on_complete=False)
    
            # Convert experiences to structured dictionaries
            experiences: List[Dict[str, Any]] = [
                {
                    "position_title": exp.position_title,
                    "company": exp.institution_name,
                    "from_date": exp.from_date,
                    "to_date": exp.to_date,
                    "duration": exp.duration,
                    "location": exp.location,
                    "description": exp.description,
                }
                for exp in person.experiences
            ]
    
            # Convert educations to structured dictionaries
            educations: List[Dict[str, Any]] = [
                {
                    "institution": edu.institution_name,
                    "degree": edu.degree,
                    "from_date": edu.from_date,
                    "to_date": edu.to_date,
                    "description": edu.description,
                }
                for edu in person.educations
            ]
    
            # Convert interests to list of titles
            interests: List[str] = [interest.title for interest in person.interests]
    
            # Convert accomplishments to structured dictionaries
            accomplishments: List[Dict[str, str]] = [
                {"category": acc.category, "title": acc.title}
                for acc in person.accomplishments
            ]
    
            # Convert contacts to structured dictionaries
            contacts: List[Dict[str, str]] = [
                {
                    "name": contact.name,
                    "occupation": contact.occupation,
                    "url": contact.url,
                }
                for contact in person.contacts
            ]
    
            # Return the complete profile data
            return {
                "name": person.name,
                "about": person.about,
                "experiences": experiences,
                "educations": educations,
                "interests": interests,
                "accomplishments": accomplishments,
                "contacts": contacts,
                "company": person.company,
                "job_title": person.job_title,
                "open_to_work": getattr(person, "open_to_work", False),
            }
        except Exception as e:
            return handle_tool_error(e, "get_person_profile")
  • The registration function that defines and registers the get_person_profile tool using the @mcp.tool() decorator inside it.
    def register_person_tools(mcp: FastMCP) -> None:
        """
        Register all person-related tools with the MCP server.
    
        Args:
            mcp (FastMCP): The MCP server instance
        """
  • Top-level MCP server creation where register_person_tools is called to register the get_person_profile tool among others.
    def create_mcp_server() -> FastMCP:
        """Create and configure the MCP server with all LinkedIn tools."""
        mcp = FastMCP("linkedin_scraper")
    
        # Register all tools
        register_person_tools(mcp)
        register_company_tools(mcp)
        register_job_tools(mcp)
  • Import of the register_person_tools function used to register the tool.
    from linkedin_mcp_server.tools.person import register_person_tools
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does (retrieves profile data) and mentions the return format, but doesn't disclose authentication needs, rate limits, privacy considerations, or what happens with invalid usernames. The description adds basic context but lacks important operational details.

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 perfectly structured with a clear purpose statement followed by organized Args and Returns sections. Every sentence earns its place by providing essential information without redundancy. The formatting with clear sections makes it easy to parse.

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?

Given the tool has an output schema (which handles return value documentation) and only one parameter, the description provides adequate context. It explains what the tool does and provides parameter examples, though it could benefit from more behavioral context about authentication or error handling for a profile lookup tool.

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

Parameters4/5

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

The schema has 0% description coverage, so the description must compensate. It provides a clear example of the single parameter ('stickerdaniel', 'anistji') that helps understand the expected format, though it doesn't explain username validation rules or where to find usernames. This adds meaningful value beyond the bare schema.

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 'specific person's LinkedIn profile', making the purpose explicit. It distinguishes from siblings like get_company_profile and get_job_details by specifying it's for a person's profile rather than company or job data.

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

Usage Guidelines4/5

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

The description implies usage context by specifying it's for retrieving a specific person's profile, but doesn't explicitly state when to use this versus alternatives like search_jobs or get_recommended_jobs. It's clear this is for looking up individual profiles rather than broader searches or job listings.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Logos-Parthenos-AI/linkedin-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server