Update the status of a tracked job application.
AI agents use update_application_status to create or update resources in LinkedIn Model Context Protocol (MCP) Server — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your LinkedIn Model Context Protocol (MCP) Server environment.
This tool creates or modifies data (application status) in a reversible manner—status changes can typically be undone by updating to a different status. It is not destructive (no deletion), not execute-class (no code/command execution), and not financial (no money movement).
From the tool's definition Tool name 'update_application_status' and description 'Update the status of a tracked job application' indicate modification of existing application records.
Documented attack patterns abuse exactly the kind of access update_application_status 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 update_application_status:
{
"version": "1",
"default": "deny",
"tools": {
"update_application_status": {
"limits": [
{
"counter": "update_application_status_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}update_application_status stays usable, but capped — an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.
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Update the status of a tracked job application. It is categorised as a Write tool in the LinkedIn Model Context Protocol (MCP) Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the LinkedIn Model Context Protocol (MCP) Server MCP server in PolicyLayer and add a rule for update_application_status: 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.
update_application_status is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the update_application_status 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 update_application_status. 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.
update_application_status 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.
Free to start. No card required.
13 LinkedIn Model Context Protocol (MCP) Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.