About Factorio MCP Server
Despite its Factorio name, the README describes a minimal TypeScript MCP starter server, not Factorio functionality. It includes a greeting example and development guidance for building and testing tools with Claude.
Key features
- 01Example greeting tool
- 02TypeScript and esbuild setup
- 03Development and watch commands
- 04MCP Inspector testing guidance
- 05Instructions for adding tools
Use cases
- 01Start a basic MCP server project
- 02Test tool interactions with Claude
- 03Develop and inspect tools locally
MCP support
The starter exposes a `hello_tool` that accepts a name and returns a greeting to MCP clients. It includes guidance for configuring Claude Desktop and testing with MCP Inspector.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEFactorio MCP Server
this doesnt work lol
- todo
- use this instead https://github.com/teoxoy/factorio-blueprint-editor/blob/master/packages/editor/
- generate an image after every request
MCP Starter Server
A minimal ModelContextProtocol server template for building AI assistant tools. This starter provides a basic structure for creating MCP tools that can be used with AI assistants like Claude.
Features
- Simple "hello world" tool example
- TypeScript + esbuild setup
- Development tools preconfigured
Setup to build and run with Claude
-
Download and install Claude desktop app from claude.ai/download
-
Clone the repo, install dependencies and build:
npm install
npm run build
- Configure Claude to use this MCP server. If this is your first MCP server, in the root of this project run:
echo '{
"mcpServers": {
"mcp-starter": {
"command": "node",
"args": ["'$PWD'/dist/index.cjs"]
}
}
}' > ~/Library/Application\ Support/Claude/claude_desktop_config.json
This should result in an entry in your claude_desktop_config.json like:
"mcpServers": {
"mcp-starter": {
"command": "node",
"args": ["/Users/matt/code/mcp-starter/dist/index.cjs"]
}
}
If you have existing MCP servers, add the mcp-starter block to your existing config. It's an important detail that the args is the path to <path_to_repo_on_your_machine>/mcp-starter/dist/index.cjs.
-
Restart Claude Desktop.
-
Look for the hammer icon with the number of available tools in Claude's interface to confirm the server is running.
-
If this is all working, you should be able to develop your MCP server using
npm run devand test it in Claude. You'll need to restart Claude each time to restart the MCP server.
Developing with Inspector
For development and debugging purposes, you can use the MCP Inspector tool. The Inspector provides a visual interface for testing and monitoring MCP server interactions.
Visit the Inspector documentation for detailed setup instructions.
To test locally with Inspector:
npm run inspect
To build on file changes run:
npm run watch
Or run both the watcher and inspector:
npm run dev
Publishing
Once you're ready to distribute your server, it's simple!
-
Set up an NPM account.
-
Run
npm publish. This will publish a package using the project name inpackage.json -
Once published, others can install the server with a config entry like:
"mcpServers": {
"<your-package-name>": {
"command": "npx",
"args": ["<your-package-name>"]
}
}
Available Tools
The server provides:
hello_tool: A simple example tool that takes a name parameter and returns a greeting
Creating New Tools
To add new tools:
- Define the tool schema in
index.ts - Add it to the tools array in the
ListToolsRequestSchemahandler - Add the implementation in the
CallToolRequestSchemahandler
See the hello_tool implementation as an example.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.