Enables AI agents to monitor and debug browser runtime errors, console logs, and page diagnostics in real time via a Chrome extension and local MCP server.
A Playwright-based MCP server that exposes a live browser as a traceable, inspectable, debuggable and controllable execution environment for AI agents.
Enables tracing mcp-use tool callbacks and capturing tool errors and exceptions in Sentry via MCP middleware, without SDK changes or additional services.
Lets an AI assistant inspect a running Google Chrome or Microsoft Edge browser over the Chrome DevTools Protocol, reading console output, network traffic, DOM snapshots, errors, performance metrics, storage, and screenshots. It also supports live event subscriptions, screen-frame streaming, and one-shot export of everything into an offline-readable archive such as HAR, CSV, and a self-contained HTML report.
Enables AI assistants to search, analyze, and debug application API traffic captured by Tusk Drift, including HTTP requests, database queries, distributed traces, latency metrics, and error rates.
Fetches Langfuse observability traces directly into a VS Code coding agent's context, enabling querying and viewing trace data through natural language.
Enables an AI agent to perform read-only inspection of Palo Alto Networks Panorama and PAN-OS firewalls for monitoring and troubleshooting, including inventory, health, network, logs, and policy queries. It communicates via the PAN-OS XML API over HTTPS and does not allow configuration changes.
Enables MCP-compatible clients to inspect the local machine's CPU, memory, and disk information through tools, a structured resource, and an upgrade-assessment prompt.
Privacy friendly, cookieless web analytics built MCP-first.
"Add analytics to my Next.js app" → an AI agent runs the
setup_analytics_for_site tool, picks the right install snippet,
edits your layout file, and verifies the script is loading.
OAuth onboarding, no API keys to paste.
Enterprise AI operator evaluation MCP server. 27 tools
(22 read + 5 write) for measuring, benchmarking, diagnosing, and interv
ening on how human operators use AI tools across 5 canonical metrics.
Enables grading a website's security headers with per-gap fixes and a README badge, and analyzing logs, code, configs or suspicious messages to determine what happened, how severe it is, and what to fix first. Requests are masked for sensitive data and automatically retried across multiple AI providers when one refuses, subject to a defensive-use-only policy.
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.