"AWS SDK or tool for managing EC2 instances and configuration" matching MCP servers:

Enables running Jupyter notebooks on temporary AWS compute (EC2 + SSM) with cost estimation, dry-run planning, progress reporting, and automatic cleanup.

Turns any OpenAPI spec into a single-tool MCP server where the model writes JavaScript against a typed api object in a QuickJS sandbox, so multi-step tasks run in one round trip and only the final result reaches context. Credentials, rate limiting, retries, write gating and audit logging stay on the host, outside the sandbox.

Facilitates interaction with AWS Bedrock-enabled tools by implementing Anthropic's Model Context Protocol for seamless tool integration and communication.

Gives LLM agents spatial awareness inside Autodesk Maya, enabling scene inspection and measurement, aesthetic and engineering audits, camera planning, checkpoint/rollback safety, asset import, and viewport or render previews. Agents can also execute arbitrary Python in Maya to build and verify 3D scenes against engineering rules instead of writing blind.

A Model Context Protocol server that provides Claude with a dedicated space for structured thinking during complex problem-solving tasks, helping improve its reasoning capabilities.

Enables Large Language Models to access and manipulate Autodesk Revit models through a pyRevit-based bridge and the Model Context Protocol.

MCP server connecting Claude/Cursor to Codex CLI, enabling code analysis via @ file references, multi-turn conversations, sandboxed edits, and structured change mode.

Enables AI assistants to leverage Qwen's code analysis capabilities with large context windows, supporting file/directory analysis, sandbox execution, and multiple approval modes for safe code operations.

A Model Context Protocol server that enables Claude to execute Python code using boto3 to query and manage AWS resources directly from conversations.

MCP server that integrates Google Gemini CLI with AI assistants, enabling large file analysis and natural language queries without an API key.

A Model Context Protocol server that enables AI assistants to interact with Google Gemini CLI, allowing them to leverage Gemini's large token window for analyzing files and codebases using natural language commands.

Enables on-demand generation of TypeScript tools using an LLM with human-in-the-loop approval and safe sandboxed execution. It allows users to create and persist custom functionality through natural language requests.

A simple MCP server implementation in TypeScript that communicates over stdio, allowing users to ask questions that end with 'yes or no' to trigger the MCP tool in Cursor.

Enables AI assistants to interact with multiple AI models through the OpenCode CLI with a unified interface. Supports plan mode for structured analysis, flexible model selection, and natural language queries with file references.

Exposes Vercel AI SDK Core capabilities (generateObject, generateText, streamText, UI generation) to AI development environments like Cursor, designed to work in orchestration with other MCP servers for multi-step AI workflows.

An MCP server that allows Claude Code to interact with the OpenAI Codex CLI.

This is a Model Context Protocol (MCP) server that allows AI assistants to interact with the OpenCode CLI. It enables AI assistants to leverage multiple AI models through a unified interface, with features like plan mode for structured thinking and extensive model selection.

A FastMCP server that dynamically loads tools from Python/YAML file pairs, acting as a stand-in for the Tools Framework in agentic-assembly-framework.

An MCP (Multi-Agent Conversation Protocol) Server that enables interaction with AWS S3 Control API through natural language, auto-generated using AG2's MCP builder.

MCP server that gives AI agents isolated, named Linux computers with stable IDs and lifecycle control. Supports multiple backends like Docker, Fly Machines, Modal, and E2B, with a dashboard for managing sandboxes.

Enables AI agents to invoke a local Stata installation, including a Windows Stata build under Wine on Linux and macOS, for regression analysis, econometrics, paper replication, and empirical research with command guards, resource monitoring, and automatic log capture.

Provides comprehensive documentation about AWS AgentCore framework to GenAI tools, enabling users to build production-ready AI agents with enterprise-grade security, observability, and scalability. Offers guidance on identity management, API integration, monitoring, code execution, memory storage, and tool integration for AI agents.

Enables AI agents to interact with the Roark API by writing and executing TypeScript SDK code in an isolated sandbox, with built-in documentation search to guide those tasks.

Enables agents to synthesize typed MCP tool schemas at runtime, validate Python ASTs for unsafe imports, compile sandboxed callable handlers, and hot-plug them into an active MCP runtime. It provides a zero-dependency, standard-library-only MCP server for safe just-in-time tool creation and execution.

An MCP server that runs a quality gate on generated code, executing it in a sandbox and scoring it before returning the code to the user.

A Python implementation of the Model Context Protocol that enables applications to provide standardized context for LLMs, allowing developers to build servers that expose data and functionality to LLM applications.

Enable Claude (or any other LLM) to interactively debug your code (set breakpoints and evaluate expressions in stack frame). It's language-agnostic, assuming debugger console support and valid launch.json for debugging in VSCode.

MCP-ORTools integrates Google's OR-Tools constraint programming solver with Large Language Models through the MCP, enabling AI models to: Submit and validate constraint models Set model parameters Solve constraint satisfaction and optimization problems Retrieve and analyze solution

A Python implementation of the Model Context Protocol that allows applications to provide standardized context for LLMs, enabling creation of servers that expose data and functionality to LLM applications through resources, tools, and prompts.

A Model Context Protocol implementation that plays sound effects (completion, error, notification) for Cursor AI and other MCP-compatible environments, providing audio feedback for a more interactive coding experience.