About Polarsteps
Polarsteps MCP Server is an unofficial server for AI assistants querying Polarsteps travel data. It provides profile, trip, travel-statistics, and search functions through an undocumented API accessed with a Polarsteps token.
Key features
- 01Retrieve user profiles and travel metrics
- 02View trip details, timelines, and locations
- 03Search trips by destination, theme, or keywords
- 04Retrieve travel statistics and achievements
Use cases
- 01Review a user's travel statistics
- 02Look up details of a trip to a destination
- 03Search travel history by theme or keyword
MCP support
The server exposes Polarsteps profile, trip, search, and analytics functions to MCP clients. It uses an undocumented Polarsteps API and requires a Polarsteps remember_token.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
uvx 'git+https://github.com/remuzel/polarsteps-mcp'POLARSTEPS_REMEMBER_TOKENSecretLocal servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.
Tools
7Saved tool list · updated 2w ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| username | string | Yes | The username of the Polarstep user to look for. |
Details
From the project READMEWarning
This MCP server uses thepolarsteps-apipackage to access Polarsteps data through undocumented APIs. Important: Please read the associated legal disclaimer and terms of use before using this tool. By using this MCP server, you agree to the terms outlined in the API package and acknowledge the associated risks.
Polarsteps MCP Server
A Model Context Protocol (MCP) server that enables Claude and other AI assistants to access Polarsteps travel data. Query user profiles, trip details, travel statistics, and search through travel histories with natural language.
Features
- User Profiles: Get profile info, social stats, and travel metrics
- Trip Data: Access detailed trip information, timelines, and locations
- Smart Search: Find trips by destination, theme, or keywords with fuzzy matching
- Travel Analytics: Retrieve comprehensive travel statistics and achievements
Quick Start
Until I add it to PyPI, the quickest way to get started is using Smithery:
npx -y @smithery/cli install @remuzel/polarsteps-mcp --client claude
Then configure your Polarsteps token.
Configuration
You'll need your Polarsteps remember_token to authenticate API requests.
Getting Your Token
- Go to https://www.polarsteps.com/ and make sure you're logged in
- Open your browser's Dev Tools:
- Firefox: Shift+F9 → Storage tab
- Chrome: F12 → Application tab → Cookies
- Find the
remember_tokencookie for https://www.polarsteps.com - Copy the token value
Setting the Token
Set your token as an environment variable:
export POLARSTEPS_REMEMBER_TOKEN="your_remember_token_here"
Usage
With Claude Desktop
Add this configuration to your Claude Desktop settings:
{
"mcpServers": {
"polarsteps": {
"command": "uvx",
"args": ["--from", "git+https://github.com/remuzel/polarsteps-mcp", "polarsteps-mcp"],
"env": {
"POLARSTEPS_REMEMBER_TOKEN": "your_remember_token_here"
}
}
}
}
Example Queries
Once configured, you can ask Claude things like:
- "Show me travel stats for username 'johndoe'"
- "Tell me about johndoe's trip to Japan"
- "What country should johndoe add to their bucketlist?"
Local Testing
Test the MCP server locally with the inspector:
npx @modelcontextprotocol/inspector uvx --from git+https://github.com/remuzel/polarsteps-mcp polarsteps-mcp
Installation from Source
For development or manual installation:
# Clone the repository
git clone https://github.com/remuzel/polarsteps-mcp
cd polarsteps-mcp
# Setup development environment
just setup
# or without just:
uv sync --dev && uv pip install -e .
Development
Running Tests
just test
Local MCP Testing
just test-mcp
Code Formatting
just lint
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.