"NestJS framework information and resources" matching MCP servers:

A demonstration implementation of Model Context Protocol (MCP) using NestJS framework, allowing developers to build MCP-compatible applications with playground testing capabilities.

A NestJS module for building Model Context Protocol (MCP) servers using decorators to expose services as tools, resources, and prompts. It features auto-discovery, a built-in playground UI, and support for multiple transports including SSE and Stdio.

LLM routing proxy that cuts API costs 60-90% by auto-selecting the cheapest capable model across OpenAI, Anthropic, and Google. Provides stats, config, and model comparison tools via MCP.

A TypeScript framework for building Model Context Protocol (MCP) servers with automatic discovery and loading of tools, resources, and prompts.

A powerful Model Context Protocol framework that extends Cursor IDE with tools for web content retrieval, PDF processing, and Word document parsing.

A framework for building MCP servers with standardized tools, authentication, logging, and multiple transports. Enables rapid development of secure, auditable MCP tools.

A robust server-side application that implements Model Context Protocol (MCP) for file operations, providing authentication and modular REST APIs for managing files, users, and posts.

A communication framework that enables multiple AI agents to collaborate through asynchronous messaging, agent registration, and discovery. It facilitates complex task coordination and real-time discussion across distributed agent workflows using the Model Context Protocol.

A lightweight and modular framework for building MCP-based file management systems with standardized module structure, flexible configuration, and extensible functionality registration mechanisms.

An MCP server framework enabling standardized tool registration, discovery, and invocation over JSON-RPC 2.0, with support for InProcess, SSE, and WebSocket transports, plus built-in tools such as calculator, text processor, and JSON formatter.

Enables AI assistants like Claude to interact with MongoDB databases through standardized MCP tools. Supports full CRUD operations, aggregations, and natural language queries with Clean Architecture and dual transport modes (STDIO/HTTP).

Contract-driven test enforcement and reporting for LLM-generated code via MCP, VS Code, Copilot CLI, Claude, Cursor, or Python SDK.

AI-native orchestration layer with 80+ tools for task management, code editing, browser automation, terminal control, and persistent memory across CLI, local MCP, and cloud deployments.

Enables MCP clients to connect to a privacy-first, self-hostable workout planning and training log, allowing coaching agents to preview and apply program changes while accessing training data through OAuth-protected endpoints.

Provides real-time system metrics and information through a Model Context Protocol interface, enabling access to CPU usage, memory statistics, disk information, network status, and running processes.

A general-purpose MCP server that provides utility tools for echo, date/time, and file operations within Claude Code and Claude Desktop. It functions as an extensible framework designed to help developers easily build and register custom Python-based tools.

Enables comprehensive Google Meet and Calendar management through Google Calendar API v3 and Google Meet API v2, supporting calendar event creation with Meet links, space management, transcription, recording, attendance tracking, and meeting moderation controls.

A framework to deploy MCP servers on AWS using serverless services like Lambda, API Gateway, and EventBridge, with dynamic tools, prompts, and resources managed via SSM parameters and Lambda functions.

A production-grade MCP server framework providing multiple specialized servers for tabular data, documents, calendars, markdown, prompt engineering, and general utilities.

An MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.