MCP Servers
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Deploy and manage Bult.ai cloud hosting projects from MCP clients like Cursor, Claude Desktop, and Claude Code. Create projects and services, deploy GitHub repositories or Docker images, manage routes and volumes, inspect logs, and troubleshoot deployments from your AI assistant.
Product search and price comparison for AI agents. Search 100M+ products across thousands of retailers by text or image, compare live offers and prices, and turn any merchant URL into structured product data. No API key required to start; add one for unlimited use and affiliate commissions.
Cloud-native solution for LLMs and AI agents with fingerprint-isolated browser environments and automation APIs. No local deployment needed; eliminates performance bottlenecks and operational overhead so developers can focus on core logic. Features include fingerprint-isolated cloud environments with independent isolation across instances, supporting untraced automation; browser behavior simulation with precise web access, element-level screenshots, dynamic data extraction, and intelligent form filling; LLM-optimized adaptation with browser behavior trajectory simulation, DOM tree parsing optimization, and structured data output; elastic resource scheduling from single-session debugging to thousands of concurrent operations with automatic compute matching.
A Model Context Protocol (MCP) server allowing URL indexing requests via the IndexNow protocol. This server acts as a bridge, enabling MCP-compatible clients (like IDEs or AI assistants) to submit URLs to search engines using the IndexNow protocol for potentially faster content
This project is derived from the original @modelcontextprotocol/server-memory package, modified for my specific needs. Providing persistent memory capabilities for AI models through a knowledge graph. This allows models like Claude to retain and recall information across interactions.
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.
