MCP Server Components

[jayscambler]

Context & Importance

For ContextFrame to power production AI agents, it needs server components that can efficiently serve context at scale. The MCP (Model Context Protocol) integration enables standardized context delivery to any compatible agent.

Why This Matters

• Production Ready: Move from experiments to deployed agents
• Performance: Optimized context delivery for low latency
• Standardization: MCP compatibility means broad agent support
• Scale: Serve thousands of agents from one ContextFrame

Use Cases

1. Customer Support: "Serve product docs to support agents"
2. Code Assistants: "Stream codebase context to Claude/Copilot"
3. Research Assistants: "Provide paper context on-demand"

Acceptance Criteria

• ContextServer class with async architecture
• MCP protocol implementation:
  • Context discovery endpoints
  • Streaming context delivery
  • Context diff support
  • Subscription to context changes
• Agent adapters:
  • Claude adapter with optimization
  • GPT adapter with token management
  • LangChain integration
  • Generic REST adapter
• Performance features:
  • Context caching (Redis)
  • Query optimization
  • Pagination support
  • Compression options
• Monitoring:
  • Context usage metrics
  • Agent performance tracking
  • Cost attribution
• Security:
  • Authentication (API keys, OAuth)
  • Authorization (context ACLs)
  • Rate limiting
  • Audit logging

Success Metrics

• <100ms context query latency
• Support 1000+ concurrent agents
• 99.9% uptime SLA

[linear]

CFOS-28 MCP Server Components