detect_antipatterns
Detect performance & design antipatterns: N+1 queries, missing eager loading, unbounded queries, event listener leaks, circular ORM association cycles, missing FK indexes, memory leaks, god classes (>=25 methods or >=500 LOC), long methods (>=60 LOC), long parameter lists (>=6), deep nesting (>=5...
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What detect_antipatterns does on Trace
AI agents call detect_antipatterns to retrieve information from Trace without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
Why detect_antipatterns is rated Low
Tool analyzes code patterns and returns findings without modifying or executing code.
From the tool's definition Detect performance & design antipatterns; read-only; returns findings JSON
Risk signalsBulk/mass operation — affects multiple targets
Attacks that exploit this kind of access
The rule that runs detect_antipatterns safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Trace, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For detect_antipatterns, this is the rule to start with:
detect_antipatterns is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Trace, apply this rule, and every detect_antipatterns call is checked against it from then on.
Questions about detect_antipatterns
Detect performance & design antipatterns: N+1 queries, missing eager loading, unbounded queries, event listener leaks, circular ORM association cycles, missing FK indexes, memory leaks, god classes (>=25 methods or >=500 LOC), long methods (>=60 LOC), long parameter lists (>=6), deep nesting (>=5). ORM-scoped signals need an active ORM plugin; size/complexity detectors run on every symbol. For import cycles use get_circular_imports; for TODOs/debug artifacts 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.
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