Returns diagnostic information about server configuration, search paths, and any warnings from the last scan. Use this when skills aren
Part of the Agent Skill Loader server.
Free to start. No card required.
AI agents call debug_info to retrieve information from Agent Skill Loader without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.
Even though debug_info only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.
Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.
{
"version": "1",
"default": "deny",
"tools": {
"debug_info": {}
}
}See the full Agent Skill Loader policy for all 5 tools.
These attack patterns abuse exactly the kind of access debug_info gives an agent. Each links to the full case and the policy that stops it:
Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.
Returns diagnostic information about server configuration, search paths, and any warnings from the last scan. Use this when skills aren. It is categorised as a Read tool in the Agent Skill Loader MCP Server, which means it retrieves data without modifying state.
Register the Agent Skill Loader MCP server in PolicyLayer and add a rule for debug_info: 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 Agent Skill Loader. Nothing to install.
debug_info 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 debug_info 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 debug_info. 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.
debug_info is provided by the Agent Skill Loader MCP server (agent-skill-loader). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 5 Agent Skill Loader tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
4,600+ MCP servers and 31,000+ tools scanned and risk-classified.