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get_company_profile

Retrieve structured LinkedIn company profile data including optional employee information for business intelligence and research purposes.

Instructions

Get a specific company's LinkedIn profile.

Args: company_name (str): LinkedIn company name (e.g., "docker", "anthropic", "microsoft") get_employees (bool): Whether to scrape the company's employees (slower)

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
company_nameYes
get_employeesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Core handler function for get_company_profile tool. Scrapes LinkedIn company page using linkedin_scraper.Company, optionally fetches employees, extracts structured data (name, about, website, etc., showcase_pages, affiliated_companies), handles errors with handle_tool_error.
    @mcp.tool()
    async def get_company_profile(
        company_name: str, get_employees: bool = False
    ) -> Dict[str, Any]:
        """
        Get a specific company's LinkedIn profile.
    
        Args:
            company_name (str): LinkedIn company name (e.g., "docker", "anthropic", "microsoft")
            get_employees (bool): Whether to scrape the company's employees (slower)
    
        Returns:
            Dict[str, Any]: Structured data from the company's profile
        """
        try:
            # Construct clean LinkedIn URL from company name
            linkedin_url = f"https://www.linkedin.com/company/{company_name}/"
    
            driver = safe_get_driver()
    
            logger.info(f"Scraping company: {linkedin_url}")
            if get_employees:
                logger.info("Fetching employees may take a while...")
    
            company = Company(
                linkedin_url,
                driver=driver,
                get_employees=get_employees,
                close_on_complete=False,
            )
    
            # Convert showcase pages to structured dictionaries
            showcase_pages: List[Dict[str, Any]] = [
                {
                    "name": page.name,
                    "linkedin_url": page.linkedin_url,
                    "followers": page.followers,
                }
                for page in company.showcase_pages
            ]
    
            # Convert affiliated companies to structured dictionaries
            affiliated_companies: List[Dict[str, Any]] = [
                {
                    "name": affiliated.name,
                    "linkedin_url": affiliated.linkedin_url,
                    "followers": affiliated.followers,
                }
                for affiliated in company.affiliated_companies
            ]
    
            # Build the result dictionary
            result: Dict[str, Any] = {
                "name": company.name,
                "about_us": company.about_us,
                "website": company.website,
                "phone": company.phone,
                "headquarters": company.headquarters,
                "founded": company.founded,
                "industry": company.industry,
                "company_type": company.company_type,
                "company_size": company.company_size,
                "specialties": company.specialties,
                "showcase_pages": showcase_pages,
                "affiliated_companies": affiliated_companies,
                "headcount": company.headcount,
            }
    
            # Add employees if requested and available
            if get_employees and company.employees:
                result["employees"] = company.employees
    
            return result
        except Exception as e:
            return handle_tool_error(e, "get_company_profile")
  • Registers the company tools (including get_company_profile) on the MCP server instance by invoking the register_company_tools function.
    register_company_tools(mcp)
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It mentions that 'get_employees' is slower, which is useful behavioral context. However, it lacks details on rate limits, authentication requirements, error handling, or whether this is a read-only operation (implied by 'Get' but not explicit). The description doesn't contradict any annotations, but it's incomplete for a tool that likely involves web scraping.

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 well-structured and front-loaded: the first sentence states the purpose clearly. The 'Args' and 'Returns' sections are organized efficiently, with no wasted words. Every sentence adds value, such as the note on speed for 'get_employees', making it concise yet informative.

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 complexity (2 parameters, 0% schema coverage, no annotations, but has an output schema), the description is reasonably complete. It explains the parameters and return type ('Structured data from the company's profile'), and the output schema handles return values. However, it could improve by addressing authentication, error cases, or sibling tool differentiation to be fully comprehensive.

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?

Schema description coverage is 0%, so the description must compensate. It adds meaningful semantics: 'company_name' is explained as 'LinkedIn company name' with examples, and 'get_employees' is clarified with its effect on speed. This goes beyond the bare schema, though it doesn't cover all potential nuances like format constraints or default behavior details.

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 clearly states the tool's purpose: 'Get a specific company's LinkedIn profile.' It specifies the verb ('Get') and resource ('company's LinkedIn profile'), making it easy to understand. However, it doesn't explicitly differentiate from siblings like 'get_person_profile' or 'get_job_details', though the resource type (company vs. person/job) is implied.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_person_profile' for individual profiles or 'search_jobs' for job-related queries. There's no context about prerequisites, such as needing a LinkedIn session or authentication, which could be relevant given the sibling 'close_session'.

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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