ZingZing001/mcpsWithCopilot
AI-driven fuel data translation pipeline using Model Context Protocol (MCP), Microsoft Copilot Studio, Docker, and Tailscale. Converts customer fuel transaction data into COMPANY APPROVED API format using a Copilot agent that generates, validates, and deploys translation scripts.
Platform-specific configuration:
{
"mcpServers": {
"mcpsWithCopilot": {
"command": "npx",
"args": [
"-y",
"mcpsWithCopilot"
]
}
}
}Add the config above to .claude/settings.json under the mcpServers key.
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This project implements an AI-driven fuel data translation pipeline. The core concept is simple:
> A customer describes their fuel transaction data in plain English. > An AI agent figures out how to map it to the EROAD API format, > writes the conversion code, tests it, and deploys it. > After that, the conversion runs automatically with no AI involved.
The AI does the hard thinking once. After the translation script is generated and validated, it runs in the Translation Engine with no AI involvement — making it fast, cheap, and deterministic.
AI generates script → deploys to engine → engine runs forever without AIWithout this system, every new customer data format requires a developer to manually write a translation script. With this system, a user can describe their data in plain language and the AI handles the rest.
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1. User describes their data in plain English to the Copilot agent
2. Agent calls MCP #1 (EROAD) to understand:
- What fields EROAD requires
- What format each field must be in
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