Model Context Protocol (MCP) Servers

Knowledge Articles
1

Overview

Model Context Protocol (MCP) servers are components in a machine learning infrastructure that manage the contextual information needed to execute models efficiently. MCP servers maintain session states, model weights, input configurations, and other runtime metadata to ensure consistent and performant model inference.

They are particularly useful in scenarios requiring:

  • Stateful inference
  • Multi-tenant deployments
  • Efficient context reuse
  • Fast inference with large or complex models

:hammer_and_wrench: Purpose and Functionality

The core responsibilities of MCP servers include:

Function Description
Model Context Management Store and manage session-specific data such as embeddings, weights, and intermediate states.
Session Handling Maintain continuity across multiple inference requests (e.g., in NLP conversations or streaming models).
Routing Direct inference calls to the appropriate model instance and hardware resources.
Performance Optimization Reduce model loading times and improve throughput by reusing contexts.

:brick: Architecture Example

Client Request
↓
Load Balancer
↓
Inference API
↓
MCP Server (Maintains Model Context)
↓
Model Runtime/Compute Backend (CPU/XRU/GPU/Accelerator)

:counterclockwise_arrows_button: When to Use MCP Servers

  • Stateful applications (chatbots, real-time translation, etc.)
  • Multi-session inference serving
  • Large model deployments where reloading is costly
  • When inference needs to be split across sessions

:police_car_light: Common Issues and Resolutions

Issue Possible Cause Suggested Resolution
Context not found Session expired or server restarted Implement session persistence or auto-reload fallback
Increased latency Overloaded MCP or context swap Load balance MCP instances, monitor context memory
Crashes or errors Incompatible model/runtime version Check versioning and runtime compatibility
Data leakage between sessions Context isolation misconfigured Ensure session IDs are correctly managed

Monitoring and Maintenance

  • Health Checks: Use HTTP probes or agent scripts to check MCP availability.
  • Logs: Centralize logs for inference traceability and debugging.
  • Metrics: Monitor metrics like:
    • Active sessions
    • Context load/unload rates
    • Memory usage
    • Request latency
  • Scaling: Auto-scale MCP servers based on traffic and model size.

:white_check_mark: Best Practices

  • Maintain versioned context for rollback capability.
  • Use short-lived sessions where possible to free up resources.
  • Secure context access using session tokens or scoped permissions.
  • Periodically review context persistence policies to avoid memory bloat.
2 Likes