Scan for known vulnerabilities using bandit (code issues) and pip-audit (dependencies)
AI agents call python_security to retrieve information from MCP DevTools Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool performs static security scanning of code and dependency metadata. Bandit and pip-audit are passive analysis tools that report on existing vulnerabilities without executing arbitrary code, modifying project files, or triggering external side effects. The action is purely informational—querying for and reporting security issues—fitting the Read category.
From the tool's definition Tool description states it 'Scan[s] for known vulnerabilities' using bandit and pip-audit, which are static analysis tools that examine code and dependencies without modifying them. The verb 'scan' indicates read-only inspection.
Documented attack patterns abuse exactly the kind of access python_security gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and MCP DevTools Server, and nothing reaches the server without passing your rules. This is the rule we recommend for python_security:
{
"version": "1",
"default": "deny",
"tools": {
"python_security": {}
}
}python_security is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Scan for known vulnerabilities using bandit (code issues) and pip-audit (dependencies). It is categorised as a Read tool in the MCP DevTools Server MCP Server, which means it retrieves data without modifying state.
Register the MCP DevTools Server MCP server in PolicyLayer and add a rule for python_security: 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 MCP DevTools Server. Nothing to install.
python_security 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 python_security 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 python_security. 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.
python_security is provided by the MCP DevTools Server MCP server (rshade/mcp-devtools-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from MCP DevTools Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
79 MCP DevTools Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.