Get detailed information about a specific LinkedIn job posting.
AI agents call get_job_details to retrieve information from LinkedIn Model Context Protocol (MCP) Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves and queries data about a LinkedIn job posting. It has no side effects, does not create, modify, or delete data, and does not execute code or trigger external operations. It is a straightforward read operation, classifying it as Read with low severity due to minimal blast radius if misused by an AI agent.
From the tool's definition Tool name 'get_job_details' and description 'Get detailed information about a specific LinkedIn job posting' indicate retrieval of job posting data with no modification or execution of actions.
Documented attack patterns abuse exactly the kind of access get_job_details gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and LinkedIn Model Context Protocol (MCP) Server, and nothing reaches the server without passing your rules. This is the rule we recommend for get_job_details:
{
"version": "1",
"default": "deny",
"tools": {
"get_job_details": {}
}
}get_job_details is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Get detailed information about a specific LinkedIn job posting. It is categorised as a Read tool in the LinkedIn Model Context Protocol (MCP) Server MCP Server, which means it retrieves data without modifying state.
Register the LinkedIn Model Context Protocol (MCP) Server MCP server in PolicyLayer and add a rule for get_job_details: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches LinkedIn Model Context Protocol (MCP) Server. Nothing to install.
get_job_details is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the get_job_details rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for get_job_details. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
get_job_details is provided by the LinkedIn Model Context Protocol (MCP) Server MCP server (rayyan9477/linkedin_mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from LinkedIn Model Context Protocol (MCP) Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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13 LinkedIn Model Context Protocol (MCP) Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.