"browser use" matching MCP servers:

MCP server for Meshbrow that gives AI agents full browser automation capabilities with stealth anti-detection, enabling natural language control of browser sessions, data extraction, and multi-browser fleets.

Use Tavily Search to search for news pages or images.

MCP server for Lightpanda headless browser enabling ultra-fast HTML extraction, markdown conversion, and JavaScript execution with 16x lower memory footprint than Chrome.

Use Nano Banana Pro to generate image from text prompt and edit image

Lets AI agents see and control desktop applications through the accessibility layer, enabling clicking, typing, scrolling, dragging, and window/app management across macOS, Windows, and Linux entirely on the local machine.

MCP server for browser debugging, inspection, and verification that streams console logs, network errors, and user actions into AI coding assistants.

An MCP front-end for AnB. The tool surface is "use-don't-reveal": no tool returns a plaintext secret. Reveal paths require a TTY the server doesn't have, so the no-reveal guarantee is structural, not prompt-based — it holds even against a prompt-injected agent. Runs as a dedicated, narrowly-scoped AnB identity; exec is default-deny (only allowlist rules tagged scope=mcp run); command output

Decentralized Git with on-chain governance, bounties, and DAOs. Tools for repos, issues, PRs, labels, releases, bounties, and DAO proposals. Auto-wallet on first use, trust tiers, and approval mode for human-in-the-loop.

Prices your Claude Code and Codex sessions from the transcripts on disk, shows what each agent cost over any period, and tells you in plain English what would have been cheaper. CLI, Browser UI and MCP server.

SuperDev is a local-first desktop app + MCP server that gives AI coding agents (Claude Code, Codex, Cursor — anything MCP-compatible) access to the runtime: live and historical logs across services, breakpoints in already-running processes (same PID, no restart), real browser control, and DAG deploy pipelines — with human approval and an audit trail on protected writes.

Content-addressed code graph that produces ranked context for AI agents in one call. 22 MCP tools across indexing, blast radius, test scope, semantic diff, runtime traffic, and feedback-aware context packing. Incremental updates via Merkle DAG (no re-indexing). GCF wire format saves 84% tokens vs JSON

A self-hosted MCP server that provides capability packs for AI agents to perform browser, desktop, git, and code actions via schema-validated JSON calls, enabling reliable agentic workflows even with small open-weight models.

Enables coding agents to navigate and score brownfield multi-service flows by mapping use-case trees, tracing loadouts and consumers, and detecting drift.

Browser automation for AI agents via the Model Context Protocol, enabling web navigation, form filling, screenshots, and more using Chromium.

Turns any web page into clean, token-budgeted Markdown for AI agents, with no browser installation required.

Resolves desktop and brand icons to local file paths for use in Waybar, Rofi, scripts, and AI agents via an MCP stdio server with tools for resolve, prefetch, cache, sources, packs, and overrides.

Policy-gated MCP server for local-first browser automation, enabling agents to drive isolated Chromium sessions via snapshots, revision-bound refs, typed actuation, scripts, and telemetry receipts.

Enables MCP-capable agents to join Landfall incident war rooms as live investigators, contributing findings and widgets to the incident timeline in realtime while pulling other investigators' discoveries, with single-use share links or authenticated sessions.

Exposes 30+ novel-writing tools over stdio or HTTP so external agents such as Claude Code or WorkBuddy can use their own models and API keys to read chapters, write prose, manage characters, foreshadowing, story arcs and worldbuilding, run local semantic search, and launch review sub-agents. Supports one-click enabling with configurable port, token, and approval mode.

Enables AI agents to securely use API keys by storing them in an encrypted vault and injecting them on demand with user approval, without exposing the key values to the model.