Detect performance & design antipatterns: N+1 query risks, missing eager loading, unbounded queries, event listener leaks (via callSites — framework-managed listeners like Livewire/Socket.IO/NestJS gateways/Mongoose/Sequelize hooks are excluded), circular ORM association cycles, missing FK indexe...
AI agents call detect_antipatterns to retrieve information from Trace without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This is a static analysis and inspection tool that traverses the dependency graph to identify potential issues in code. It reads and analyzes code patterns to provide intelligence about performance and design problems, but does not execute code, modify code, delete code, or trigger external operations.
From the tool's definition Tool performs static analysis detection of antipatterns: 'Detect performance & design antipatterns: N+1 query risks, missing eager loading, unbounded queries, event listener leaks...' The tool analyzes and reports on code patterns without modifying,…
Risk signalsBulk/mass operation — affects multiple targets
Documented attack patterns abuse exactly the kind of access detect_antipatterns gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Trace, and nothing reaches the server without passing your rules. This is the rule we recommend for detect_antipatterns:
{
"version": "1",
"default": "deny",
"tools": {
"detect_antipatterns": {}
}
}detect_antipatterns is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
Free to start. No card required.
Detect performance & design antipatterns: N+1 query risks, missing eager loading, unbounded queries, event listener leaks (via callSites — framework-managed listeners like Livewire/Socket.IO/NestJS gateways/Mongoose/Sequelize hooks are excluded), circular ORM association cycles, missing FK indexes, memory leaks (unbounded caches, closure-captured growing collections), god classes (>=25 methods or >=500 LOC), long methods (>=60 LOC), long parameter lists (>=6 params), deep nesting (>=5 indent levels). ORM-scoped signals require an active ORM plugin; size/complexity detectors (god_class, long_method, long_parameter_list, deep_nesting) run on every indexed symbol. For ES/CJS import cycles use get_circular_imports. For code quality (TODOs, debug artifacts, hardcoded values) use scan_code_smells. For security use scan_security. Read-only. Returns JSON: { findings: [{ category, severity, file, line, message, suggestion }], total }. It is categorised as a Read tool in the Trace MCP Server, which means it retrieves data without modifying state.
Register the Trace MCP server in PolicyLayer and add a rule for detect_antipatterns: 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 Trace. Nothing to install.
detect_antipatterns 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 detect_antipatterns 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 detect_antipatterns. 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.
detect_antipatterns is provided by the Trace MCP server (nikolai-vysotskyi/trace-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 178 Trace tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
178 Trace tools catalogued and risk-classified — across an index of 42,500+ MCP servers.