MCP server for AI agent observability, providing trace and span logging, search, latency/tokens/cost metrics, and anomaly detection using an in-memory buffer.
An MCP server that records agent execution metrics and exposes a Context Window Explorer to visualize exactly what entered the model's context window across sessions, tokens, and tool calls.
An MCP server that provides cost and reliability observability for LLM and agent workflows. It records model calls and allows querying and aggregating telemetry data through MCP tools.
A local-first MCP server that gives AI coding agents runtime visibility and AI-managed debug logging. It replaces blind print() debugging by turning runtime execution into causal chains, allowing agents to instantly locate bugs by finding missing .success events in Python and TypeScript code. Single binary with MCP, CLI, and HTTP interfaces.