withings-mcp
Read access to Withings scale data, including weight and body composition metrics, through a local MCP server.
README
withings-mcp
A local MCP server that gives Claude read access to your Withings scale data (weight, and body-composition metrics like fat ratio, muscle mass, bone mass, and hydration, where your scale records them).
Setup
1. Register a Withings developer app
- Go to https://developer.withings.com/dashboard/ and sign in (or create an account).
- Create a new app.
- Set the Redirect URI to
http://localhost:8934/callback(must match exactly). - Copy the Client ID and Client Secret.
2. Install dependencies
pip install -r requirements.txt
3. Configure credentials
cp .env.example .env
Edit .env and fill in WITHINGS_CLIENT_ID and WITHINGS_CLIENT_SECRET.
4. Authorize (one time)
python auth.py
This opens your browser to log in to Withings and approve access. On success,
tokens are saved to token.json (gitignored). The server auto-refreshes the
access token from then on — you shouldn't need to run this again unless you
revoke access.
5. Register the server with Claude Code
claude mcp add withings -- python C:\Users\brian\Desktop\Claude\withings-mcp\server.py
Restart Claude Code (or reload MCP servers) afterward.
Tools exposed
get_latest_weight()— most recent weigh-inget_weight_history(start_date?, end_date?)— weigh-ins in a date range (YYYY-MM-DD), or full history if omitted
Troubleshooting
- "No saved Withings token found" — run
python auth.py. - Withings API error / status != 0 — the error body includes Withings' status
code; common ones are
401(bad params) and503(invalid/expired token — re-runpython auth.py).
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.