Get detailed information about a specific job posting.
AI agents call get_job to retrieve information from LinkedIn Intelligence 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 job posting data from LinkedIn's public or user-accessible job listings. It has no side effects, does not modify data, execute code, delete records, or move money. The blast radius of misuse is minimal; an agent could retrieve job information it shouldn't access, but cannot cause irreversible harm or financial loss. This is a straightforward Read operation.
From the tool's definition Tool description states 'Get detailed information about a specific job posting' — a retrieval operation with no modification, deletion, or execution of external actions.
Documented attack patterns abuse exactly the kind of access get_job gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and LinkedIn Intelligence MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for get_job:
{
"version": "1",
"default": "deny",
"tools": {
"get_job": {}
}
}get_job 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 job posting. It is categorised as a Read tool in the LinkedIn Intelligence MCP Server MCP Server, which means it retrieves data without modifying state.
Register the LinkedIn Intelligence MCP Server MCP server in PolicyLayer and add a rule for get_job: 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 Intelligence MCP Server. Nothing to install.
get_job 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 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. 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 is provided by the LinkedIn Intelligence MCP Server MCP server (southleft/linkedin-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 87 LinkedIn Intelligence MCP Server tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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87 LinkedIn Intelligence MCP Server tools catalogued and risk-classified — across an index of 42,500+ MCP servers.