log_recommend
Analyze codebase and recommend where to add structured logging for better observability
This record as markdown: /tools/barnburner121-plugin-hub-mcp/log-recommend.md
What log_recommend does on Plugin Hub
AI agents use log_recommend to create or update resources in Plugin Hub, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Plugin Hub environment.
Why log_recommend is rated Medium
An AI agent can call log_recommend faster than any human can review: one bad instruction and it creates or modifies resources in Plugin Hub by the hundred, each call as confident as the last.
Attacks that exploit this kind of access
The rule that runs log_recommend safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Plugin Hub, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For log_recommend, this is the rule to start with:
log_recommend 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.
The button opens the PolicyLayer dashboard: create your workspace, connect Plugin Hub, apply this rule, and every log_recommend call is checked against it from then on.
Questions about log_recommend
Analyze codebase and recommend where to add structured logging for better observability. It is categorised as a Write tool in the Plugin Hub MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Plugin Hub MCP server in PolicyLayer and add a rule for log_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 Plugin Hub. Nothing to install.
log_recommend 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 log_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.
Set action: deny in the PolicyLayer policy for log_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.
log_recommend is provided by the Plugin Hub MCP server (@barnburner121/plugin-hub-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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