tv_recommend

Get personalized recommendations based on watch history + trending.

Server Pypi:stv pypi:stv
Category Read
Risk class Low
Parameters 00 required

What tv_recommend does on Pypi:stv

AI agents call tv_recommend to retrieve information from Pypi:stv without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.

Why tv_recommend needs a policy

Even though tv_recommend only reads data, uncontrolled read access leaks sensitive information and racks up API costs — an agent caught in a retry loop can make thousands of calls a minute without anyone noticing.

Questions about tv_recommend

What does the tv_recommend tool do? +

Get personalized recommendations based on watch history + trending. It is categorised as a Read tool in the Pypi:stv MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on tv_recommend? +

Register the Pypi:stv MCP server in PolicyLayer and add a rule for tv_recommend: 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 Pypi:stv. Nothing to install.

What risk level is tv_recommend? +

tv_recommend is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit tv_recommend? +

Yes. Add a rate_limit block to the tv_recommend 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.

How do I block tv_recommend completely? +

Set action: deny in the PolicyLayer policy for tv_recommend. 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.

What MCP server provides tv_recommend? +

tv_recommend is provided by the Pypi:stv MCP server (pypi:stv). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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