mcp-templateio MCP server
If you already use MCP Templateio, the mcp templateio mcp server is the piece that lets your assistant work with it directly. mcp-templateio MCP server.
A Model Context Protocol (MCP) server built with mcp-framework that provides an image generation tool using Templated.io.
This template provides a starting point for building MCP servers with custom tools. It includes an example tool and instructions on how to add more tools, develop them, and publish them to npm. This README will guide you through the process of setting up, developing, and deploying your own MCP server.
Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client. The configuration blocks on this page cover the common clients.
Configuration is passed through the environment: TEMPLATED_API_KEY. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
Plenty of developer tooling servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. It is maintained by Lucker631; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
{
"mcpServers": {
"mcp-templateio": {
"command": "node",
"args": ["/absolute/path/to/mcp-templateio/dist/index.js"]
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| TEMPLATED_API_KEY | Credential the server authenticates with. | Yes |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.