I got tired of hosting tiny MCP servers, so I built Glxymesh

Abhishek Mourya — Thu, 08 Oct 2026 17:04:24 +0000

Every time I wanted an AI agent to call a real API, I ended up writing a small MCP server for it. Then I had to host it, handle auth, store secrets and redeploy it, all for what was often a single HTTP call.

Glxymesh is my attempt to remove that work. You describe the tool, and Glxymesh builds it, hosts it and gives your agents one authenticated endpoint.

How you create a tool

There are two ways.

1. Let it draft the tool for you. Point Glxymesh at an API's docs and it drafts the tool. You review it, approve which keys it can use, and publish.

2. Describe it yourself. Write the tool in a few lines of YAML. If it needs real logic, add a run function in Go, TypeScript or Python. Most tools that wrap an API don't need any code at all.

How you ship it

The CLI has three commands you'll use:

gxmesh init     # start a project
gxmesh new      # add a tool, then edit it in any editor
gxmesh push     # build, test and put it live

Each tool runs in its own isolated AWS Lambda function. Secrets and access control are handled for you.

How your agents use it

You get one authenticated MCP endpoint for the whole project. Point Claude, ChatGPT or Gemini at it and every tool you've published is available.

Pricing

Pricing is flat with no overage billing:

Free: 10 hosted tools
Pro: $29/month, 250 tools
Team: $79/month, 2,500 tools

I chose flat pricing on purpose. Per-request billing makes agent costs hard to predict, because you don't control how often an agent decides to call a tool.

Try it

It's live at glxymesh.com. It's early, and I'd love feedback in the comments, especially on two questions: