Why this server?
Fetches real-time documentation for Langchain, Llama-Index, MCP, and OpenAI, addressing the limited context window by allowing Claude to access external information beyond its training data.
Why this server?
Helps AI read GitHub repository structure and important files, allowing it to quickly understand the context of a repo when its context window is limited.
Why this server?
A simple aggregator server that allows batching multiple MCP tool calls into a single request, reducing token usage and network overhead which helps with context limits.
Why this server?
A feature-rich MCP server that federates MCP and REST services, providing virtualization of legacy APIs as MCP-compliant tools. It helps in managing and accessing different tools, easing the burden on context window.
Why this server?
An MCP server that extends AI agents' context window by providing tools to store, retrieve, and search memories, allowing agents to maintain history and context across long interactions.
Why this server?
A coding agent toolkit that transforms LLMs into coding assistants capable of working directly on your codebase with semantic code retrieval and editing tools, providing IDE-like capabilities without requiring API subscriptions.