"Guidance for Understanding Large Codebases and Learning Their Architecture" matching MCP servers:

Enables a browser agent to inspect, rank, preview, evaluate, and validate transformer layer pruning proposals, while ensuring only a person can commit removals.

Hey @roocode community! I'm thrilled to share a project born from my work with Roocode and the vision of an AI-powered development team: the Anubis MCP Server! This system is heavily inspired by Roocode and designed to orchestrate an AI development workflow based on agile methodology. It simulates

Enables autonomous learning from interactions through pattern recognition and machine learning techniques. Continuously improves performance by analyzing tool usage, providing predictive suggestions, and sharing knowledge across MCP servers.

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

Enables ZCode to control a real Chrome browser, including tabs, navigation, page content, clicks, form filling, keyboard input, scrolling, screenshots, and JavaScript evaluation through a local MCP bridge.

Enables AI agents to drive real Google Chrome with stealth and captcha handling, using Patchright to avoid bot detection.

Enables ChatGPT to delegate writing, analysis, second opinions, and Japanese text polishing to Gemini through a single ask_gemini tool, without copying text to Gemini's web interface. Self-hosted on Cloudflare with your own Gemini API key and Google login.

Exposes Coze workflows as MCP tools, allowing AI clients to discover and execute workflows from a specified Coze workspace. It provides a simple way to integrate external skills into platforms like OpenClaw without needing local environment configurations.

Provides a set of MCP tools (file, database, GitHub, Slack, browser, calendar, email, vector search, Python execution) with safety constraints and OpenAI integration for enterprise architecture automation.

Enables AI agents to control a Playwright-driven browser through natural-language instructions, including page interaction and live browser task execution.

Enables interaction with Cloudera Machine Learning to manage projects, files, and jobs through the Model Context Protocol. It supports tasks such as uploading files, scheduling jobs, and managing runtimes via natural language interfaces.

Enables AI agents to discover and execute tools via a secure MCP server with JWT authentication, RBAC, rate limiting, and audit logging.

Enables multi-agent design sessions with a structured workflow for defining voice, generating directions, creating UI elements, and exporting artifacts.

An authless Cloudflare Workers server that provides tools to scrape, analyze, and answer questions about websites using Cloudflare's Browser Rendering and AI capabilities.

AgentPay — x402 crypto data gateway on Stellar. 10 live pay-per-call tools: token prices, whale activity, gas tracker, DeFi TVL, Fear & Greed, Dune queries, token security. Agents pay USDC on Stellar. No API keys. Budget-aware sessions.

Allows AI to interact with Autodesk Revit via the MCP protocol, enabling retrieval of project data and automation of tasks like creating, modifying, and deleting elements.

A Model Context Protocol server implementation that enables AI assistants to interact with Slack workspaces, allowing them to browse channels, send messages, reply to threads, add reactions, and retrieve user information.

Merx - TRON Resource Exchange Merx is the first TRON resource exchange that aggregates energy and bandwidth providers into a single MCP server. GitHub: https://github.com/Hovsteder/merx-mcp Hosted SSE: https://merx.exchange/mcp/sse npm: merx-mcp Website: https://merx.exchange Stats * 52 tools, 30 prompts, 21 resources * 7+ energy providers (CatFee, ITRX, PowerSun, Fe

MCP Server for Google Cloud Healthcare API enables Agentic AI for a variety of FHIR-based digital health solutions, from smarter clinical workflows for Health Systems to Pre-Auth frameworks for Payers!

An MCP server implementation that integrates AI assistants with Langfuse workspaces, allowing models to query LLM metrics by time range.