MCP observability

MCP Observability provides comprehensive monitoring for Model Context Protocol (MCP) implementations, enabling AI models to securely connect to data sources and interact with external tools.

Overview

The MCP Observability dashboard monitors Model Context Protocol performance and usage:

Protocol health - Session management and connection stability monitoring
Tool analytics - Usage patterns and performance metrics for tool operations
Transport monitoring - Communication layer performance across different protocols
Performance tracking - End-to-end MCP operation latency and efficiency

Key features

Protocol metrics

Session establishment - Connection setup success rates and duration
Connection stability - Session duration and reliability tracking
Protocol compliance - Adherence to MCP standards and version compatibility
Message throughput - Communication volume and payload analysis

Tool usage analytics

Tool invocation frequency - Most and least used tools and usage patterns
Tool performance - Response times and success rates by tool type
Tool popularity trends - Usage patterns and adoption over time
Tool error analysis - Failure categorization and root cause identification

Transport performance

Communication latency - Response times across different transport types
Network reliability - Connection stability and retry patterns
Client-server interaction - Communication patterns and optimization opportunities