"Using Python to Locate and Retrieve Browser Element Selectors" matching MCP servers:

Enables autonomous agents to anchor browser elements from vision inputs, providing stable DOM references resilient to click drift for reliable computer-use automation.

Enables AI agents to anchor visible on-screen elements to stable DOM targets, preventing click drift during browser computer-use automation.

Browser use using Typesafe's Jev model

Enables secure execution of Python code in a sandboxed WebAssembly environment using Pyodide and Deno. Automatically handles package management and captures complete execution results including stdout, stderr, and return values.

Enables MCP clients to run complete browser workflows through a single run_workflow tool, with anti-detection backends, intelligent element location, automatic retries, and session persistence.

Lightweight Python Notebook MCP - Enable AI assistants to create, edit, and view Jupyter notebooks via Model Context Protocol

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Self-hosted MCP server for AI browser automation. Connects to your own Chromium instance via CDP, providing tools for browser control, navigation, interaction, and content extraction.

Gives an AI assistant a persistent, stealth-configured Chrome browser that stays logged in across sessions, supports interactive first login, and can autofill credentials from the macOS Keychain and replay WebAuthn passkeys for sites that block scripted login.

MCP server for AI browser automation with tools for navigation, actions, data extraction, and scripting, supporting local and cloud execution.

Enables AI agents to navigate the web visually using screenshot-based interaction and Set-of-Mark labeling for interactive elements. It supports humanized browsing behaviors, anti-detection measures, and complex tasks like multi-click CAPTCHA solving.

Enables AI agents to break down complex tasks into manageable pieces using a structured JSON format with task tracking, context preservation, and progress monitoring capabilities.

An MCP server that enables AI agents to control a real Chrome browser with full login state through 37 structured automation tools, connecting via the Playwriter extension without needing a remote debugging port.

A Model Context Protocol server that converts Markdown content to HTML format.

A Machine Comprehension Protocol server that enables AI assistants to interact with Kestra workflows through natural language, supporting operations like flow management, executions, backfills, and other Kestra features.

Enables AI agents to control a persistent Chrome browser through a local daemon, with Jev performing routine actions and the host agent handling reasoning, text, and verification.

Enables browser automation through MCP clients like Claude or Cursor, using the client's existing LLM without requiring an additional API key.

It lets LLM clients drive a browser by specifying outcomes while a TypeSafe System One model chooses each action and Playwright executes it. It also provides tools for checking page state, selecting among options, snapshots, and direct actions.

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.