What is HexaGO?
HexaGO is a small implementation of hexagonal architecture (ports and adapters) in Go, illustrating a calculator app.
@edlingao
Basic information
This is just me playing around with an MCP
No standard config provided
This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.
RepositoryTool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
HexaGO is a simple example of a calculator app using the ports and adapter (hexagonal) architecture in Go. It demonstrates how to separate business logic (core) from external systems via ports and adapters.
Clone the repository, install air and templ, then run air in the project root. Open your browser and go to http://localhost:3000.
HexaGO is a small implementation of hexagonal architecture (ports and adapters) in Go, illustrating a calculator app.
Clone the repo, install air and templ, run `air` in the root, then open http://localhost:3000.
You need Go, `air` (go install github.com/cosmtrek/air@latest), and `templ` (go install github.com/a-h/templ/cmd/templ@
Mobbin MCP connects your AI agents to 600,000+ real product screens, so what they build starts with what already works.

Launch your product from your AI agent. Connect Claude Code, Codex, Cursor or ChatGPT to Launchdog over MCP and your agent drafts the listing, picks a launch week and books it. 50 launches a week, ranked only by human vo

Agent-Native Terminal

AI-native payments for tools and APIs. Discover, pay for, execute, and publish monetized APIs directly from your agent, settled per call in USDC over x402, through the Model Context Protocol. payments · finance · x402 ·

ArtiBot Cloud MCP (Zhijiangyun 智匠云) — cloud MCP server for robot manipulation data & GPU workloads. 15 tools: robot fleet management, data collection & video recording, post-processing pipelines, and benchmark evaluation
Comments