"Developing an API using NestJS, Prisma, and PostgreSQL" matching MCP servers:

Manage Prisma Postgres databases with ease

Enables AI agents to interact with existing NestJS backend services via MCP, providing tools to look up clients, list products, and create requests.

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.

MCP server that helps AI agents inspect Minecraft project evidence (crash logs, mod files, datapacks) before writing development code.

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

Build MCP servers inside NestJS applications using ordinary controllers with decorators for tools, resources, and prompts, supporting authentication and authorization.

Give your AI coding agents the superpower to observe your NestJS application's runtime state in real-time.

Publishes a spending-summary tool alongside an interactive HTML view, letting people click a category in the answer to fetch the individual expense records behind that total. Keeps calculations and their supporting evidence together in a single inspectable flow.

Read-only MCP server for PostgreSQL, enabling schema discovery, table metadata, and safe SELECT queries via READ ONLY transactions.

A production-ready NestJS backend template with built-in MCP server enabling AI agents to query your data via standardized protocol, featuring RAG pipeline, semantic search, and comprehensive developer tooling.

A production-ready MCP server that connects AI assistants to any PostgreSQL database with 25 tools and role-based access control.

Provides AI-driven PostgreSQL database management with secure OAuth 2.1 authentication, enabling users to administer, monitor, and query databases with support for extensions like pgvector, PostGIS, and pg_cron.

MCP server for managing notes with CRUD tools. It uses the same notes service as the GraphQL API, enabling natural language interaction with notes.

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.

Full-featured MCP server that exposes 36 tools for interacting with PostgreSQL databases, covering schema introspection, query execution, data exploration, performance monitoring, security auditing, and maintenance.

A Model Context Protocol server that enables interaction with PostgreSQL databases for analyzing setups, debugging issues, managing schemas, migrating data, and monitoring performance.

Enables agents and IDEs to generate and verify NestJS microservices with hexagonal architecture, JSON:API contracts, and mandatory security headers. Supports declarative spec-based module generation, refactoring existing services, quality checks, and per-repository inventory and auditing.

A Model Context Protocol server that enables powerful PostgreSQL database management capabilities including analysis, schema management, data migration, and monitoring through natural language interactions.

Facilitates management and optimization of PostgreSQL databases, offering analysis, setup guidance, and debugging, while ensuring secure and efficient database operations.

Free DEX aggregator API that returns executable swap calldata across 46 EVM chains. No API key required. Single GET request returns ready-to-sign transactions for any token pair.

Enables LLM clients such as Cursor and Claude to discover and use generated REST services through MCP tools for create, update, and delete actions and MCP resources for list and get-by-id context. Services generated in one command are exposed simultaneously as REST endpoints and MCP tools and resources.

Enables AI assistants to interact with generated REST APIs by discovering and using CRUD tools and read-only data resources.

Enables AI assistants to discover and invoke CRUD tools and read-only resources over the Model Context Protocol for generated API services.

Enables AI assistants to interact directly with generated REST APIs by exposing CRUD operations as MCP tools and read-only data as resources.

Exposes generated CRUD services to LLM clients over the Model Context Protocol, providing tools for create, update, and delete actions plus read-only resources for listing and fetching records by id. It lets assistants like Claude, ChatGPT, and Cursor discover and manipulate the same data served by the project's REST endpoints without any extra glue code.

Exposes every generated CRUD service to LLM clients such as Claude, ChatGPT, or Cursor through discoverable MCP tools for create, update, and list actions plus read-only resources, so models can query and manipulate data without raw HTTP calls. It runs alongside the same codebase's REST endpoints, letting clients and AI share one data layer.

Enables LLMs to discover and invoke generated CRUD operations as MCP tools and read data through MCP resources, alongside standard REST endpoints, using one codebase.

Enables LLMs to discover and invoke generated CRUD tools and resources, allowing AI assistants to interact with the same data exposed by the REST API. Supports MCP clients such as Cursor and Claude over HTTP.

Enables LLMs to discover and invoke generated API actions as MCP tools and read context through MCP resources for list and get-by-id operations. It provides HTTP-based MCP access for clients such as Cursor and Claude without extra glue code.