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Search Jobs

search_jobs
Read-only

Find job opportunities on LinkedIn by entering specific search terms to match career interests and qualifications.

Instructions

Search for jobs on LinkedIn using a search term.

Args: search_term (str): Search term to use for the job search.

Returns: List[Dict[str, Any]]: List of job search results

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
search_termYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • Core handler function implementing the search_jobs tool logic: initializes driver, performs job search via linkedin_scraper JobSearch, converts results to list of dicts, handles errors.
    async def search_jobs(search_term: str) -> List[Dict[str, Any]]:
        """
        Search for jobs on LinkedIn using a search term.
    
        Args:
            search_term (str): Search term to use for the job search.
    
        Returns:
            List[Dict[str, Any]]: List of job search results
        """
        try:
            driver = safe_get_driver()
    
            logger.info(f"Searching jobs: {search_term}")
            job_search = JobSearch(driver=driver, close_on_complete=False, scrape=False)
            jobs = job_search.search(search_term)
    
            # Convert job objects to dictionaries
            return [job.to_dict() for job in jobs]
        except Exception as e:
            return handle_tool_error_list(e, "search_jobs")
  • Schema definition via ToolAnnotations: sets title 'Search Jobs' and operation hints (readOnly, destructive, openWorld).
    @mcp.tool(
        annotations=ToolAnnotations(
            title="Search Jobs",
            readOnlyHint=True,
            destructiveHint=False,
            openWorldHint=True,
        )
    )
  • Registration of job tools: imports register_job_tools and calls it on the FastMCP instance to register search_jobs and related tools.
    from linkedin_mcp_server.tools.company import register_company_tools
    from linkedin_mcp_server.tools.job import register_job_tools
    from linkedin_mcp_server.tools.person import register_person_tools
    
    logger = logging.getLogger(__name__)
    
    
    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)
Behavior4/5

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

The description adds value beyond annotations by specifying the platform ('on LinkedIn') and the return type ('List of job search results'). Annotations already indicate read-only, non-destructive, and open-world behavior, so the bar is lower. The description doesn't contradict annotations and provides useful context about the data source and output format.

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 appropriately sized and front-loaded with the core purpose in the first sentence. The Args and Returns sections are structured but could be more integrated. It avoids unnecessary details, though the formatting is slightly verbose for a single parameter.

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's low complexity (1 parameter), rich annotations (readOnlyHint, openWorldHint), and presence of an output schema, the description is mostly complete. It covers the purpose, parameter, and return value. However, it lacks usage guidelines compared to siblings, which is a minor gap in this context.

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 0%, but the description compensates by explaining the single parameter 'search_term' in the Args section. However, it doesn't add meaning beyond what the schema title ('Search Term') implies, such as examples or constraints. With one parameter and no schema descriptions, baseline is 3 as it minimally documents the parameter.

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: 'Search for jobs on LinkedIn using a search term.' It specifies the verb ('Search'), resource ('jobs on LinkedIn'), and mechanism ('using a search term'). However, it doesn't explicitly differentiate from sibling tools like 'get_recommended_jobs' or 'get_job_details', which would require a 5.

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_recommended_jobs' (for personalized results) or 'get_job_details' (for specific job info), nor does it specify contexts or exclusions for usage.

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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