"Understanding Frontend and Backend Development" matching MCP servers:

MCP server providing backend access to PostgreSQL, Storage (Supabase/S3), Iceberg data lake, and SQL seeds. It offers 32 tools for database queries, storage operations, seed management, and more.

A comprehensive MCP toolkit for Java backend developers, providing 35 tools across 5 servers for database analysis, JVM diagnostics, migration assistance, Spring Boot monitoring, and Redis diagnostics.

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 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.

Connects ElevenLabs voice agents to Supabase for real-time order lookups, enabling voice agents to retrieve order status, tracking, and delivery information through the lookup_order tool.

Enables AI assistants to interact with Databricks workspaces, running SQL queries, managing jobs, and exploring schemas via the Model Context Protocol.

Reusable MCP server for Python backend development with FastAPI, MongoDB, and Stackraise support.

An MCP server designed to interact with the RifaExpress backend via PostgreSQL database queries and REST API integrations. It enables users to manage core entities like companies, users, and plans while providing tools for database schema inspection and health monitoring.

Search the fastest-growing US horse boarding directory: 6,000 facilities and 3,800 trainers across 48 states, plus boarding price data by state.

It enables AI assistants to discover and invoke CRUD operations as MCP tools and read application data through MCP resources, allowing natural-language interaction with the generated backend.

Enables AI agents to interact with Magento 2 development environments through comprehensive tools for module management, database operations, cache control, configuration management, and system diagnostics. Supports complete development workflows from module creation to deployment and troubleshooting.

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.

Enables LLMs and MCP clients to discover and use generated CRUD services through MCP tools and resources, exposing actions like create, update, delete, and read-only list/get access alongside REST endpoints.

Enables AI assistants to discover and invoke generated CRUD tools and read-only resources, letting them interact directly with REST-backed services built on Express, Mongoose, or Sequelize.

Enables LLMs and AI assistants to discover and use tools and resources for CRUD operations on generated services, providing direct interaction with the same data exposed by REST endpoints.

Enables LLMs to discover and invoke CRUD tools and read-only resources for generated services, allowing direct interaction with the same data exposed by the REST API.

Enables LLMs to interact with generated REST API services by exposing MCP tools and resources for CRUD operations, listing, and retrieval. It lets AI clients discover and call create, update, delete, list, and get-by-id actions against the same data available over REST.

Exposes every generated Express service to LLM clients over HTTP as MCP tools (create, update, delete actions) and resources (list and get-by-id context), so AI assistants can discover and manipulate application data without raw HTTP calls. This lets teams run a single codebase that serves both traditional REST clients and AI agents such as Cursor or Claude.

Enables LLMs to interact with generated CRUD services through MCP tools and resources, so they can create, update, list, and fetch records via a standard protocol. It also provides REST API access from the same codebase for traditional clients.

Enables LLM clients such as Claude, ChatGPT, and Cursor to discover and act on generated CRUD services over HTTP through tools like create, update, list, and delete, plus read-only resources for listing and fetching records by ID, with no extra glue code.

An MCP server that gives LLM clients access to a SQL Server database and the local filesystem, including file operations and image handling.

Convex’s MCP server lets you introspect tables, call functions, and read/write data seamlessly. Agents can generate one-off queries safely—thanks to Convex’s sandboxed queries, ensuring data integrity. Perfect for AI automation, real-time apps, and dynamic data access.