USDA Nutrition MCP Server - Model Context Protocol server for USDA FoodData Central API integration
USDA Nutrition MCP Server - Model Context Protocol server for USDA FoodData Central API integration. That is what the mcp nutrition tools mcp server brings to an AI assistant: the same capability, reachable through the Model Context Protocol rather than a separate app or dashboard.
This project showcases professional MCP implementation skills:
Configuration is passed through the environment: FDC_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.
Because this one is hosted, setup is mostly authentication — you point your client at the endpoint and approve access. Nothing runs on your machine, so there is no runtime to keep patched.
Among the developer tooling options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. It is maintained by zen-apps; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against MCP Nutrition Tools's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
### Option 2: Local Development
```json
{
"mcpServers": {
"usda-nutrition": {
"command": "python3",
"args": [
"/path/to/mcp-nutrition-tools/src/mcp_bridge.py",
"--server-url",
"http://localhost:8080"
],
"cwd": "/path/to/mcp-nutrition-tools"
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| FDC_API_KEY | Credential the server authenticates with. | Yes |
A knowledge graph your assistant keeps between sessions — entities, relations and observations that persist.
Kill hallucinated APIs — version-accurate, up-to-date library documentation injected straight into context.
Your workspace, on speaking terms with AI — search, read and write Notion pages and databases.
A structured scratchpad for hard problems — stepwise reasoning with revisions, branches and visible logic.
Symbol-level code navigation, refactoring and memory for coding agents — the IDE brain your assistant has been missing.
Chat with your second brain — search, read and write vault notes through the Local REST API.