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LangGraph Nutrition Analyzer

This nutrition analysis MCP server by sunilpowar5 provides food image recognition and nutritional data retrieval through Google's Gemini AI and the Nutritionix API, featuring a LangGraph-powered workflow that identifies food items from uploaded images and fetches detailed calorie and protein information. Built with FastAPI backend serving nutrition_fetch and wiki_search tools, and a Streamlit frontend for interactive image upload and follow-up questions, it uses Google Generative AI for food identification with quantity estimation and integrates Wikipedia search for additional nutritional context. The implementation leverages LangGraph's state management for conversational workflows, supports both initial image analysis and follow-up queries about nutrition facts, and includes comprehensive error handling with retry mechanisms, making it valuable for health-conscious users tracking dietary intake, nutritionists analyzing meal compositions, and fitness applications requiring automated food logging capabilities.

Cognium trust score

99%

Tier

Verified

Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-28.

Scan details: Circle-IR · 2026-09-28 · Appeal

View full trust & usage report →

Metadata

Version: 1.0.0
Skill type: atomic
Execution layer: mcp-remote
Category: media
Source: PulseMCP
Repository: github.com/sunilpowar5/langgraph-mcp-nutrition-analyzer
Author type: human
Last scanned: 2026-09-28
Updated: 2026-09-28

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MCP

Resolve LangGraph Nutrition Analyzer from your agent

Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.

One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.

claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp

Swap --scope user for --scope project to commit it to .mcp.json.

Your client Same endpoint everywhere; only the config key differs. The full list is in the descriptor.

Claude Desktop · Settings → Connectors → Add custom connector

https://api.skillsregistry.net/mcp

Paste the URL as the connector endpoint. No authentication.

ChatGPT · Settings → Connectors → Create (developer mode) → MCP server URL

https://api.skillsregistry.net/mcp

Authentication: none. Works in chat and deep research — the server exposes the connector search and fetch tools.

Codex CLI · ~/.codex/config.toml

[mcp_servers.skillsregistry]
url = "https://api.skillsregistry.net/mcp"