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stickerdaniel

LinkedIn MCP Server

by stickerdaniel
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Get Recommended Jobs

get_recommended_jobs
Read-only

Retrieve personalized job recommendations from LinkedIn based on your profile and preferences to identify relevant career opportunities.

Instructions

Get your personalized recommended jobs from LinkedIn

Returns: List[Dict[str, Any]]: List of recommended jobs

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • linkedin_mcp_server/tools/job.py:104-128 (handler)
    The async handler function that implements the core logic of get_recommended_jobs by using JobSearch scraper configured to fetch recommended jobs.
    async def get_recommended_jobs() -> List[Dict[str, Any]]:
    """
    Get your personalized recommended jobs from LinkedIn

    Returns:
        List[Dict[str, Any]]: List of recommended jobs
    """
    try:
        driver = safe_get_driver()

        logger.info("Getting recommended jobs")
        job_search = JobSearch(
            driver=driver,
            close_on_complete=False,
            scrape=True,  # Enable scraping to get recommended jobs
            scrape_recommended_jobs=True,
        )

        if hasattr(job_search, "recommended_jobs") and job_search.recommended_jobs:
            return [job.to_dict() for job in job_search.recommended_jobs]
        else:
            return []
    except Exception as e:
        return handle_tool_error_list(e, "get_recommended_jobs")
  • linkedin_mcp_server/tools/job.py:96-103 (registration)
    The @mcp.tool decorator registers the get_recommended_jobs function as an MCP tool with appropriate annotations and metadata.
    @mcp.tool(
    annotations=ToolAnnotations(
        title="Get Recommended Jobs",
        readOnlyHint=True,
        destructiveHint=False,
        openWorldHint=True,
    )
)
  • linkedin_mcp_server/server.py:30-30 (registration)
    Invocation of register_job_tools which registers the job tools including get_recommended_jobs to the MCP server instance.
    register_job_tools(mcp)

Tool Definition Quality

B3.4/5.0
Behavior3/5

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

Annotations already provide readOnlyHint=true and destructiveHint=false, so the agent knows this is a safe read operation. The description adds that it returns 'personalized recommended jobs' which provides useful context about the data source and personalization aspect. However, it doesn't mention authentication requirements, rate limits, or what 'personalized' means in practice.

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 concise with two sentences that directly address what the tool does and what it returns. The first sentence states the purpose, and the second describes the return format efficiently. No wasted words or unnecessary elaboration.

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 zero-parameter read operation with comprehensive annotations and an output schema, the description provides adequate context. It explains what data is retrieved and the return format. The main gap is lack of differentiation from sibling tools, but given the structured metadata coverage, this is reasonably complete.

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?

With 0 parameters and 100% schema description coverage, the baseline would be 4. The description appropriately doesn't discuss parameters since there are none, and the schema already fully documents the empty input structure.

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 action ('Get') and resource ('personalized recommended jobs from LinkedIn'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'search_jobs' or 'get_job_details' - it mentions personalization but not how this differs from other job-related tools.

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 like 'search_jobs' or 'get_job_details'. It mentions 'personalized recommended jobs' but doesn't explain what triggers this personalization or when an agent should prefer this over other job-finding methods.

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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