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The MCP Gateway required a robust plugin framework to support AI safety middleware, security processing, and extensible gateway capabilities. The implementation needed to support both self-contained plugins (running in-process) and external middleware service integrations while maintaining performance, security, and operational simplicity.
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The MCP Gateway required a robust plugin framework to support AI safety middleware, security processing, and extensible gateway capabilities. The implementation needed to support both native plugins (running in-process) and external middleware service integrations while maintaining performance, security, and operational simplicity.
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## Decision
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## Performance Characteristics
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-**Latency Impact:**Self-contained plugins add <1ms overhead per hook
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-**Latency Impact:**Native plugins add <1ms overhead per hook
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-**Memory Usage:**~5MB base overhead, scales linearly with active plugins
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-**Throughput:** Tested to 1000+ req/s with 5 active plugins
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-**Context Cleanup:** Automatic cleanup every 5 minutes, contexts expire after 1 hour
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### Positive
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✅ **Complete AI Safety Pipeline:** Framework supports end-to-end content filtering and safety
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✅ **High Performance:**Self-contained plugins provide sub-millisecond latency
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✅ **High Performance:**Native plugins provide sub-millisecond latency
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✅ **Operational Simplicity:** File-based configuration integrates with existing workflows
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✅ **Future-Proof:** Architecture supports both current needs and roadmap expansion
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✅ **Security-First:** Multiple layers of protection against malicious plugins and inputs
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❌ **Debugging Challenges:** Sequential plugin chains can be difficult to troubleshoot
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### Neutral
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🔄 **Hybrid Architecture:** Both self-contained and external services require different operational approaches
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🔄 **Hybrid Architecture:** Both native and external services require different operational approaches
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