AI agents call get_posts to retrieve information from Outx without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool queries and retrieves LinkedIn posts from existing watchlists without creating, modifying, or deleting any data. It is a passive read operation with filtering/search capabilities. No side effects or irreversible actions are possible.
From the tool's definition Tool description explicitly states 'Search and filter LinkedIn posts' and 'Returns 20 posts per page' — retrieving data with no modification or deletion
Attacks that exploit this kind of access
Search and filter LinkedIn posts from your watchlists. Supports keyword search, date range, seniority, trending, language, and more. Returns 20 posts per page. It is categorised as a Read tool in the Outx MCP Server, which means it retrieves data without modifying state.
Register the Outx MCP server in PolicyLayer and add a rule for get_posts: 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 Outx. Nothing to install.
get_posts 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_posts 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_posts. 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_posts is provided by the Outx MCP server (outx-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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