"A tool for finding errors in the console or browser during frontend development" matching MCP servers:

Give any LLM agent a real Android or iPhone. 62 MCP tools: tap, swipe, type, screenshot, screen-tree reading, app launch, camera, TTS, crash reports, batched execution. Android via ADB, iPhone via WebDriverAgent, on-device inference, Docker+KVM emulators. Works with Claude Code, Cursor, LangChain, LlamaIndex, and any MCP client. MIT.

Enables AI assistants like Claude to interact with humans through intuitive GUI dialogs, supporting text input, choices, confirmations, and information displays.

Enables AI agents to call tools through a security gateway that intercepts requests, enforces deterministic policy and risk checks, blocks or escalates high-risk actions for human approval, and keeps tool credentials and audit logs protected.

Enables structured learning with a verified loop: define goals as observable claims, learn through teach-lab-test-gate per claim, and get independently graded by an adversarial examiner to ensure genuine progress.

Enables agents to synthesize typed MCP tool schemas at runtime, validate Python ASTs for unsafe imports, compile sandboxed callable handlers, and hot-plug them into an active MCP runtime. It provides a zero-dependency, standard-library-only MCP server for safe just-in-time tool creation and execution.

Enables agents to dynamically generate typed MCP tool schemas, validate Python execution ASTs against unsafe imports, and hot-plug sandboxed callable handlers into an active runtime. This allows new tools to be synthesized and safely executed on demand.

Enables an Alexa+ host to place and manage customer-care calls by building a case, navigating phone menus, waiting on hold, explaining the issue, guarding sensitive disclosures, handing off identity checks, recording ticket numbers and promises, and scheduling follow-ups.

An MCP server that enables AI agents to pause and request human approval or information via Slack, Telegram, or macOS dialogs before proceeding with actions.

Implements Anthropic's 'think' tool for Claude, providing a dedicated space for structured reasoning during complex problem-solving tasks that improves performance in reasoning chains and policy adherence.

An MCP relay that adds a use_jev tool to every connected server, letting the Jev model autonomously select and execute tool calls while the calling model supplies any required text values. It enables agents to delegate multi-step tool use across all configured MCP servers through a single unified interface.

An MCP server implementation of Anthropic's Think Tool prompt engineering technique that enables Claude to break down complex problems and enhance its reasoning capabilities by providing a simple tool that echoes back thoughts.

MCP server for The Commons (jointhecommons.space), a persistent, noncommercial space where AI voices from different models post and reply to each other with persistent identities. 48 tools; reading needs no token, writing uses a facilitator-issued token.

Enables AI agents to control your installed Google Chrome through MCP or HTTP using compact text snapshots, exact pixel coordinates, and a visible cursor. It provides browser tools for navigation, clicking, typing, scrolling, tabs, forms, waiting, recording, and human handoff.

A server built on mcp-framework that enables integration with Claude Desktop through the Model Context Protocol.

Gives AI agents a single shared, logged-in Chrome they can drive over MCP or plain HTTP: open and read tabs, click, type, upload files, run short Playwright scripts, hand off whole web tasks to a cheaper model, and keep per-site memory. Agents work in background windows they own without stealing the user's mouse or closing their tabs.

An MCP server that implements the 'think' tool, providing Claude with a dedicated space for structured thinking during complex problem-solving tasks to improve reasoning capabilities.

Enables MCP clients to automate a real browser by stating one outcome at a time in natural language, with a decision model (Jev) choosing each element, action, and value while code drives Playwright. Also exposes tools to open pages, verify questions, read page content, snapshot elements, act directly, and call a page's WebMCP tools.

Enables governed browser automation for AI agents with human-in-the-loop approval, audit logging, and read-only replay, supporting both ASK and AUTO autonomy modes.

Enables AI agents to drive the Chrome, Brave, or Edge browser you are already logged into, working in a tab of its own with a visible cursor and real trusted input. You can pause, take over, or hand back control at any time, and consequential actions such as submitting, paying, sending, deleting, or uploading wait for your approval.

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

Provides a 'reflect' tool that creates cognitive checkpoints for AI assistants, forcing structured step-by-step reasoning through complex problems to improve accuracy and maintain context during task execution.

Enables AI agents to control a real, persistent Chrome browser over MCP, perceiving pages as accessibility trees and interacting with elements by their accessible names without automation fingerprints or site-specific glue.

Connects AI agents to The Agents Hub, visualizing them as pixel characters on a tile-based property with tools for state, assets, inboxes, and multi-agent orchestration.

A secure tool-execution plane for agentic AI that enforces JWT authentication, rate limiting, prompt-injection inspection, and audit logging, while ingesting downstream OpenAPI endpoints as MCP tools.

Enables MCP clients to automate a Playwright browser by having an LLM specify step outcomes while a decision model selects elements, actions, values, and statuses, with optional WebMCP tool invocation.

Enables an MCP client to drive a real browser by stating one outcome at a time, while a Typesafe System One model decides which element, action, and value to use and whether the step is done, blocked, errored, or irreversible, with Playwright executing each action. It also lets callers take over directly for page snapshots, question-targeted reads, screenshots, raw element actions, and invoking the page's own WebMCP tools without any model call.

Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.

Enables LLM clients like Claude to drive a real Playwright browser by stating one outcome at a time, while a TypeSafe Jev model decides which element, action, and value to use each round, returning a status of done, stuck, or needs-confirmation. Also exposes tools for direct element actions, page checks, snapshots, text reading, screenshots, and calling a page's own WebMCP tools.

Enables autonomous agents and MCP-compatible clients to enforce policy-approved actions using risk tiers, reversible checkpoints, and token approval bounds.

Self-host a realtime voice-call companion for coding agents. Exposes an MCP endpoint that agents can poll as an alternate input stream.