MCP Servers: Context Hosting and Management

How servers manage model contexts, handle requests, and maintain state in the MCP framework.

Introduction

MCP servers are responsible for storing, retrieving, and updating context frames for AI models. They act as the central point for multi-turn interactions, memory management, and resource control.

Server Implementation

• Context Storage: Persist context frames in memory or database for multi-turn sessions.
• Request Handlers: Process queries, retrieve relevant context, and return updated frames.
• Concurrency: Support multiple sessions simultaneously with thread-safe memory operations.
• Error Handling: Detect invalid frames, corrupted memory, or resource exhaustion gracefully.
• Integration APIs: Provide endpoints for clients to read, write, or modify context frames securely.

Server Configuration

• Memory Limits: Define maximum context frames per session or per user.
• Persistence: Choose between in-memory, database, or hybrid storage.
• Security: Authentication and authorization for accessing context frames.
• Monitoring: Track memory usage, request throughput, and session health.
• Scaling: Horizontal scaling for handling high numbers of concurrent sessions.

Example Server Flow

1. Client sends a request with a session ID and current context frame.
2. Server retrieves the frame, applies updates or reasoning, and stores any changes.
3. Server returns the updated frame to the client.
4. Monitoring tools log memory usage and session performance.

Conclusion

MCP servers ensure consistent context management, resource optimization, and multi-session support. They are essential for building scalable, stateful AI applications that rely on context-aware reasoning.