#REPL

Mirror of

Mirror of

A collection of standalone Python scripts that implement Model Context Protocol (MCP) servers for various utility functions. Each server provides specialized tools that can be used by AI assistants or other applications that support the MCP protocol.

🌐Web Agent Protocol (WAP) - Record and replay user interactions in the browser with MCP support

A Model Context Protocol (MCP) server for analyzing OpenReplay session recordings and user behavior patterns

A Java-based Model Context Protocol (MCP) server that provides deep introspection, monitoring, debugging, and REPL capabilities for Java applications. Descartes enables AI assistants to interact with running Java processes through a comprehensive set of tools and resources. Kind of does for the Java backend what Playwright does for the frontend. Easy to add as a maven dependency to your Java application.

Sunra.ai is a generative media platform built for developers, providing high-performance AI model inference capabilities. It specializes in running diffusion models through production-ready APIs and interactive UI playgrounds.

Run & Manage Long Processes - install, scrap, encode video, compile Search & Read & Edit files - including diff editing for speed and precision

MCP for Replicate Flux Model - A powerful tool for generating customized images and SVG assets that match specific coding vibes and aesthetic styles. Streamline your visual asset creation process with AI-powered design generation tailored for developers.

A Model Context Protocol server that executes commands in the current iTerm session - useful for REPL and CLI assistance

The Rollbar MCP Server implements the Model Context Protocol (MCP), letting you run Rollbar-compatible agents in local or custom environments.

MCP server for deAPI — 35 AI tools: transcription, image/video generation, TTS, music, OCR, embeddings via deAPI.

Unified inbox MCP for WhatsApp, Telegram, Email, voice — read/send messages, search conversations, manage contacts, query knowledge graph, and trigger AI agents through natural language.

peek records the user's actual logged-in browser (DOM via rrweb, console events, network metadata, optional response bodies via opt-in Deep capture) through a Chrome MV3 extension. The extension ships events through a native-messaging stdio bridge to a local MCP server (peek-mcp), which persists them to a SQLite database at ~/.peek/sessions.db. AI coding agents (Claude Code, Cursor, Cline, Windsurf) read sessions from the database via 10 MCP tools: Tool What it does list_recent_sessions List recently recorded sessions (id, origin, ts, event count). get_session_summary LLM-readable narrative summary of a session. get_session_console_errors Console errors recorded in a session. get_session_network_errors Failed/notable network requests in a session. get_user_action_before_error Last N user actions before a console error. generate_playwright_repro Generate a runnable Playwright test from a session. get_dom_snapshot Reconstruct the DOM at a given timestamp. query_dom_history Timeline of attribute/text changes for a selector. request_authorization Side-panel consent for write actions (Level 3). execute_action Dispatch a UI action (gated by permission level + destructive blocklist). Why local-first matters Every other "browser session for AI" tool ships to a vendor cloud. peek's SQLite + extension live on the user's machine — no remote endpoints, no telemetry. The privacy policy (docs/peek/PRIVACY_POLICY.md) is the source of truth. Install # 1. Add the MCP server to Claude Code claude mcp add peek -- npx -y @peekdev/mcp # 2. Install the Chrome extension from the Chrome Web Store # (link added once the CWS listing is approved)