"Developer tools for a NextJS and FastAPI tech stack with Firebase and Vercel" matching MCP servers:

Enables interaction with Firebase projects and services, including deployment, database management, authentication, and local emulation, through natural language.

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

A Model Context Protocol server that exposes Firebase Firestore and Authentication to AI agents, supporting multi-project configurations and read operations.

The Firebase MCP server provides a standardized interface to interact with Firebase services, including Firebase Authentication, Firestore, and Firebase Storage.

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

A server providing a unified interface to interact with Firebase services, including Authentication, Firestore, and Storage.

Enables inventory management through FastAPI with AI chat powered by Ollama, allowing natural language interaction with PostgreSQL data via MCP for clients like Claude Desktop and VS Code.

A general-purpose MCP server for Firebase that enables natural language interaction with Firestore, Auth, Storage, and Cloud Functions logs through schema-driven validation and context-efficient tools.

Enables AI assistants to work directly with Firebase realtime database

A bridge that enables Large Language Models to read from and write to Firebase Firestore collections through Model Context Protocol (MCP) tools.

A read-only MongoDB MCP server designed for serverless deployment on Vercel, providing secure, limited access to MongoDB databases for AI assistants.

Enables AI agents to interact with Firebase services including Authentication, Firestore Database, and Cloud Storage through a standardized MCP protocol.

A Django app that implements Firebase Model Context Protocol server, enabling AI agents to interact with Firebase services (Authentication, Firestore Database, Cloud Storage) through a standardized protocol.

Exposes Firebase emulator data (Firestore, Auth, Realtime Database, and logs) to AI coding assistants for local development inspection.

Enables secure, read-only access to Firebase Firestore collections and Storage buckets for Digital Asset Management systems. It allows users to query assets, versions, and comments, and search storage files using flexible filters through standard MCP tools.

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.

A minimal CRUD API for items that exposes FastAPI endpoints as MCP tools, enabling natural language interaction with a database through PydanticAI agents.

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.