List all rendered clip files in the output directory with file sizes and dates.
AI agents call list_outputs to retrieve information from Podcli without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves directory contents and file metadata without any side effects, creation, modification, deletion, or execution of code. It is a straightforward read operation querying the output directory state.
From the tool's definition Tool description states 'List all rendered clip files in the output directory' - a pure query/list operation with no modification or execution capability. Returns metadata (file sizes and dates) only.
Documented attack patterns abuse exactly the kind of access list_outputs gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Podcli, and nothing reaches the server without passing your rules. This is the rule we recommend for list_outputs:
{
"version": "1",
"default": "deny",
"tools": {
"list_outputs": {}
}
} list_outputs is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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List all rendered clip files in the output directory with file sizes and dates. It is categorised as a Read tool in the Podcli MCP Server, which means it retrieves data without modifying state.
Register the Podcli MCP server in PolicyLayer and add a rule for list_outputs: 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 Podcli. Nothing to install.
list_outputs 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 list_outputs 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 list_outputs. 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.
list_outputs is provided by the Podcli MCP server (nmbrthirteen/podcli). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Podcli, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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17 Podcli tools catalogued and risk-classified — across an index of 43,000+ MCP servers.