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

A template for deploying MCP servers on Vercel with serverless functions. Includes example tools for rolling dice and getting weather data to demonstrate basic functionality and API integration patterns.

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

A template for deploying MCP servers on Vercel with example tools for rolling dice and fetching weather data. Provides a starting point for building custom MCP servers with TypeScript.

A high-performance Model Context Protocol (MCP) server designed for large language models, enabling real-time communication between AI models and applications with support for session management and intelligent tool registration.

A simple server that acts as a Master Control Program (MCP) for unified interaction with OpenAI and Anthropic (Claude) AI models through a single API endpoint.

Exposes Vercel AI SDK Core capabilities (generateObject, generateText, streamText, UI generation) to AI development environments like Cursor, designed to work in orchestration with other MCP servers for multi-step AI workflows.

Enables offline AI agent automation with embedded local LLM (Qwen 2.5), sandboxed file operations through AgentFS, and dynamic skill loading. Exposes capabilities via MCP with tri-state safety guards for private, air-gapped environments without network connectivity or API costs.

A Model Context Protocol server designed to facilitate Nextflow development and testing, providing tools for building from source, running tests, and managing the Nextflow development environment.

An MCP server that provides a persistent sandbox for AI coding agents to explore codebases server-side, returning only compact summaries to reduce context consumption.

Enables Large Language Models to access and manipulate Autodesk Revit models through a pyRevit-based bridge and the Model Context Protocol.

A minimal MCP server that exposes a run_command tool over stdio, enabling command execution within a Docker container.

Gives any MCP-compatible AI chat or agent a safe, model-neutral coding runtime with file read/search, structured multi-file patches, command execution, interactive sessions, and git operations, all confined to a single workspace and gated by permission modes.

A zero-configuration tool that automatically exposes FastAPI endpoints as Model Context Protocol (MCP) tools, allowing LLM systems like Claude to interact with your API without additional coding.

A production-ready foundation template for building Model Context Protocol (MCP) servers with FastAPI, featuring modular tools, comprehensive testing, and OpenShift deployment configurations. Includes automated transformation scripts to create custom domain-specific MCP servers.

A starter template for deploying Model Context Protocol (MCP) servers on Vercel using TypeScript and Vercel Functions. It includes example tools for rolling dice and checking weather to demonstrate tool integration patterns.

Provides agents with real Linux VMs to run commands, manage files, expose ports, and track costs, all through an ephemeral sandbox service.

A template for deploying MCP servers on Vercel with example tools for rolling dice and getting weather data. Provides a foundation for building custom MCP servers with tools, prompts, and resources that can be deployed to Vercel's serverless platform.

Enable Claude (or any other LLM) to interactively debug your code (set breakpoints and evaluate expressions in stack frame). It's language-agnostic, assuming debugger console support and valid launch.json for debugging in VSCode.

A meta-MCP server that orchestrates tools from multiple MCP servers, enabling complex Python workflows with loops and conditionals.

Enables AI agents to execute tools securely with Docker isolation, tier-based permissions, and feature flags, providing a centralized tool registry and MCP server.