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Why this server?
This server provides the ability to fetch web content in various formats, which could be helpful for gathering runway-related information.
Why this server?
This server provides rich tool capabilities for AI assistants reducing prompt token consumption, which can be helpful in dealing with runway specific requests.
Why this server?
This server allows converting HTTP API into MCP tool, allowing to configure MCP server to use runway data from HTTP endpoints.
Why this server?
This toolkit offers a variety of functionalities like file system access and GitHub repository interaction, making it a comprehensive choice.
Why this server?
This server makes documentation searchable by AI assistants, allowing users to chat with code or docs from a git repository or folder related to runway.
Why this server?
This server exposes multiple AI tools over SSE transport with JWT-based secure authentication, allowing for dynamic tool registration and session management.
Why this server?
This meta-server allows Claude to install other MCP servers from npm or PyPi, enabling easy expansion of capabilities with external tools.
Why this server?
A simple TypeScript library for creating Model Context Protocol (MCP) servers with features like type safety, parameter validation, and a minimal code API.
Why this server?
A lightweight MCP server that provides a unified interface to various LLM providers including OpenAI, Anthropic, Google Gemini, Groq, DeepSeek, and Ollama.
Why this server?
An open standard server implementation that enables AI assistants to directly access APIs and services through Model Context Protocol, built using Cloudflare Workers for scalability.