"Ram" matching MCP servers:

An MCP server that provides a persistent sandbox for AI coding agents to explore codebases server-side, returning only compact summaries to reduce context consumption.

Integrates the Qwen3-Coder 30B parameter model with Claude Code through 5 specialized tools for code review, explanation, generation, bug fixing, and optimization. Optimized for 64GB RAM systems with advanced performance settings including flash attention and parallel processing.

MCP server that lets Claude analyze huge files (logs, dumps, etc.) via a sandboxed Python REPL and recursive language models, keeping content out of the prompt. Zero-setup with Claude Code login, Docker sandbox by default, and tools for loading, searching, chunking, and querying large contexts.

Enables AI assistants to analyze documents larger than their context window by loading files into RAM and querying them via search, navigation, and Python execution tools. Supports recursive reasoning to process massive datasets in chunks using sub-agents.

An implementation of the Recursive Language Models architecture that enables AI agents to process massive documents by programmatically decomposing them into sub-queries. It allows for cost-effective and accurate reasoning across millions of tokens by treating long-form data as an external environment for root and worker models.

MCP server that enables verified code execution with LLM reasoning using Recursive Language Models (RLM). It supports tasks like code generation, data analysis, and complex task decomposition.

Enables the analysis of massive datasets by storing data in external variables via a sandboxed Python REPL instead of the model's context window. This allows users to process files larger than 100MB without polluting the context or hitting token limits.

A Model Context Protocol server that enables users to control and interact with Google Colab instances via a reverse proxy. It supports executing shell commands, running persistent Python code, and monitoring system resources like CPU, GPU, and RAM.

Implements a RAG workflow that integrates with any custom knowledge base and can be triggered directly from the Cursor IDE.

Enables AI assistants to read, edit, and execute Jupyter notebook files (.ipynb) with live kernel output, without requiring JupyterLab.

Speeds up coding agents by predicting and pre-executing upcoming tool calls, then safely returning validated cached results to eliminate wait time.

Enables Claude to generate executable test cases from decision tables in CSV, JSON, or Markdown formats. Provides intelligent test planning guidance and generates Playwright/API test code with TypeScript support.

Enables users to create, manage, and execute Google Apps Script projects through natural language. It provides comprehensive tools for code editing, function execution, deployment management, and monitoring script processes.

A Model Context Protocol (MCP) server that bridges MCP clients with AWS Lambda functions, enabling AI models to invoke Lambda functions as tools without code changes.

A Node.js package providing Model Context Protocol server infrastructure for AWS Lambda functions with streaming response capabilities through Server-Sent Events.

Agentic RAG server for document management, hybrid search, question answering with citations, and analytical code execution, powered by LanceDB and Pydantic AI.