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.
Context intelligence for AI coding sessions. 7 MCP tools to score, compare, compress, build, and scan prompts across 9 AI tools. Rule-based, <5ms/prompt, all analysis runs locally.
A 100% local development monitoring tool that captures browser console logs, network requests, and backend server output for analysis by AI assistants via MCP. It enables LLMs to debug applications by providing structured, real-time access to full-stack log data and persistent local storage.
Local-first MCP server that gives AI assistants codebase intelligence—code graph, drift analysis, vulnerability attribution, and version-correct library docs—all from the user's machine.
Provides real-time monitoring of AI agents, context, usage limits, workflows, files, Git, tests, builds, errors, secrets, and model-economy advice for tools like Claude Code, Codex, and Cursor, with 30 MCP tools for comprehensive observability.
Local production engineering platform that indexes Python codebases and exposes semantic code analysis, git risk scoring, and log correlation through MCP tools for AI assistants.
