"NestJS Framework for Building Backend Applications" 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.

Embeds a runtime MCP inspector in a NestJS app so coding agents can inspect the module and provider graph, routes and their guards/interceptors/pipes/filters, logs, request traces, event listeners, and scheduled jobs through an HTTP endpoint.

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.

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

Provides real-time access to Japanese building standards laws and regulations from e-Gov API, helping prevent AI hallucinations by validating citations against original legal texts.

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

Drives Android, iOS, smart TV (Android TV, Tizen, webOS), web and Bluetooth (BLE) devices from an agent: start a session, tap, type, swipe and assert by XPath, visible text, OCR or image, then save the steps as a replayable CSV test suite.

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

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

Enables AI agents to read Microsoft Fabric lakehouse or warehouse tables, infer keys and relationships, and create or update a GraphQL API item — returning a ready endpoint for Lovable, Cursor, or Claude Code. It also plans and validates schemas and drafts companion artifacts such as semantic models, dbt sources, join paths, and data dictionaries, with dry-run defaults.

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 FastMCP-powered server for accessing curated travel spot data from a PostgreSQL database. It provides tools to retrieve major attractions, supporting sites, and the top ten most popular destinations.

Provides comprehensive building and office address information queries for enterprises in China, including fuzzy enterprise search, office address details and statistics, and building information queries to support geographical distribution and occupancy analysis.

Gives AI assistants a read-only, offline interface to the FinOps Foundation's published guidance — its principles, phases, domains, 22 capabilities with Crawl/Walk/Run maturity assessments, personas, and an 88-entry KPI library — so FinOps questions are answered with the Foundation's own text and formulas rather than improvised ones. It exposes 11 tools, document resources, and four guided prompts over stdio, with every response carrying its source URL and license.

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.

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).

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.

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

Enables LLMs to interact with Kibana through composable toolboxes for building dashboards, managing data, and handling alerts, among other operations.

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.