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Intervals.icu MCP 服务器

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mvilanova

Model Context Protocol (MCP) server for connecting Claude and ChatGPT with the Intervals.icu API.

Publishermvilanova
Repositoryintervals-mcp-server
LanguagePython
Forks
91
Stars
271
Available tools
0
Transport typestdio
Categories
LicenseGPL-3.0
Links
  • Connect tools to AI workflows

    Intervals.icu MCP 服务器 exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 0 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    271 stars and 91 forks from the linked repository.

Intervals.icu MCP Server

Model Context Protocol (MCP) server for connecting Claude and ChatGPT with the Intervals.icu API. It provides tools for authentication and data retrieval for activities, events, wellness data, power curves, and custom items.

If you find the Model Context Protocol (MCP) server useful, please consider supporting its continued development with a donation.

Requirements

Setup

1. Install uv (recommended)

macOS/Linux:

bash
curl -LsSf https://astral.sh/uv/install.sh | sh

Windows (PowerShell):

powershell
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

After installation, find the full path to uv — you'll need it later when configuring Claude Desktop:

powershell
where.exe uv
# Example output: C:\Users\<USERNAME>\.local\bin\uv.exe

2. Clone this repository

bash
git clone https://github.com/mvilanova/intervals-mcp-server.git
cd intervals-mcp-server

3. Create and activate a virtual environment

bash
# Create virtual environment with Python 3.12
uv venv --python 3.12

# Activate virtual environment
# On macOS/Linux:
source .venv/bin/activate
# On Windows:
.venv\Scripts\activate

4. Sync project dependencies

bash
uv sync

5. Set up environment variables

Make a copy of .env.example and name it .env by running the following command:

macOS/Linux:

bash
cp .env.example .env

Windows (PowerShell):

powershell
Copy-Item .env.example .env

Then edit the .env file and set your Intervals.icu athlete id and API key:

API_KEY=your_intervals_api_key_here
ATHLETE_ID=your_athlete_id_here

Getting your Intervals.icu API Key

  1. Log in to your Intervals.icu account
  2. Go to Settings > API
  3. Generate a new API key

Finding your Athlete ID

Your athlete ID is typically visible in the URL when you're logged into Intervals.icu. It looks like:

  • https://intervals.icu/athlete/i12345/... where i12345 is your athlete ID

Updating

This project is actively developed, with new features and fixes added regularly. To stay up to date, follow these steps:

1. Pull the latest changes from main

⚠️ Make sure you don't have uncommitted changes before running this command.

macOS/Linux:

bash
git checkout main && git pull

Windows (PowerShell):

powershell
git checkout main; git pull

2. Update Python dependencies

Activate your virtual environment and sync dependencies:

macOS/Linux:

bash
source .venv/bin/activate
uv sync

Windows (PowerShell):

powershell
.venv\Scripts\activate
uv sync

Troubleshooting

If Claude Desktop fails due to configuration changes, follow these steps:

  1. Delete the existing Intervals.icu entry in claude_desktop_config.json.
  2. Reconfigure Claude Desktop from the intervals-mcp-server directory.

macOS/Linux:

bash
mcp install src/intervals_mcp_server/server.py --name "Intervals.icu" --with-editable . --env-file .env

Windows: Re-add the entry manually as described in the Windows configuration section.

Common errors

spawn uv ENOENT — Claude Desktop cannot find the uv executable. Use the full path to uv in the command field. Run which uv (macOS/Linux) or where.exe uv (Windows) to get it.

spawn /Users/... ENOENT on Windows — The config file contains a macOS/Linux-style path. Replace it with the correct Windows path using backslashes as described in the Windows configuration section below.

Windows Store install: config changes not taking effect — You may be editing the wrong config file. Claude Desktop installed from the Microsoft Store reads from AppData\Local\Packages\Claude_pzs8sxrjxfjjc\LocalCache\Roaming\Claude\claude_desktop_config.json, not AppData\Roaming\Claude\.

Usage with Claude

1. Configure Claude Desktop

To use this server with Claude Desktop, you need to add it to your Claude Desktop configuration.

macOS/Linux

  1. Run the following from the intervals-mcp-server directory to configure Claude Desktop:
bash
mcp install src/intervals_mcp_server/server.py --name "Intervals.icu" --with-editable . --env-file .env
  1. If you open your Claude Desktop App configuration file claude_desktop_config.json, it should look like this:
json
{
  "mcpServers": {
    "Intervals.icu": {
      "command": "/Users/<USERNAME>/.local/bin/uv",
      "args": [
        "run",
        "--with",
        "mcp[cli]",
        "--with-editable",
        "/path/to/intervals-mcp-server",
        "mcp",
        "run",
        "/path/to/intervals-mcp-server/src/intervals_mcp_server/server.py"
      ],
      "env": {
        "INTERVALS_API_BASE_URL": "https://intervals.icu/api/v1",
        "ATHLETE_ID": "<YOUR_ATHLETE_ID>",
        "API_KEY": "<YOUR_API_KEY>",
        "LOG_LEVEL": "INFO"
      }
    }
  }
}

Where /path/to/ is the path to the intervals-mcp-server code folder in your system.

Windows

The mcp install command may fail on Windows due to environment or permission issues. Instead, configure Claude Desktop manually:

  1. Find the Claude Desktop config file. If Claude Desktop was installed from the Microsoft Store, the config is located at:

    C:\Users\<USERNAME>\AppData\Local\Packages\Claude_pzs8sxrjxfjjc\LocalCache\Roaming\Claude\claude_desktop_config.json

    If installed via the standard installer, it may be at:

    C:\Users\<USERNAME>\AppData\Roaming\Claude\claude_desktop_config.json

    If the file or folder does not exist, create it.

  2. Add the following entry to claude_desktop_config.json, replacing the placeholders with your actual values:

json
{
  "mcpServers": {
    "Intervals.icu": {
      "command": "C:\\Users\\<USERNAME>\\.local\\bin\\uv.exe",
      "args": [
        "run",
        "--with",
        "mcp[cli]",
        "--with-editable",
        "C:\\path\\to\\intervals-mcp-server",
        "mcp",
        "run",
        "C:\\path\\to\\intervals-mcp-server\\src\\intervals_mcp_server\\server.py"
      ],
      "env": {
        "INTERVALS_API_BASE_URL": "https://intervals.icu/api/v1",
        "ATHLETE_ID": "<YOUR_ATHLETE_ID>",
        "API_KEY": "<YOUR_API_KEY>",
        "LOG_LEVEL": "INFO"
      }
    }
  }
}
  • Use double backslashes (\\) for all Windows paths in JSON.
  • To find the full path to uv.exe, run where.exe uv in PowerShell.
  • To find the full path to the cloned repository, run pwd from inside the intervals-mcp-server folder.

Note for Windows Store installs: Claude Desktop installed from the Microsoft Store sandboxes its config under AppData\Local\Packages\.... Editing AppData\Roaming\Claude\claude_desktop_config.json will have no effect — make sure you edit the correct file.

  1. Restart Claude Desktop.

2. Use the MCP server with Claude

Once the server is running and Claude Desktop is configured, you can use the following tools to ask questions about your past and future activities, events, and wellness data.

  • get_activities: Retrieve a list of activities
  • get_activity_details: Get detailed information for a specific activity
  • get_activity_intervals: Get detailed interval data for a specific activity
  • get_activity_streams: Get raw data streams (power, heart rate, etc.) for a specific activity
  • get_athlete_power_curves: Get best power output curves for selected durations and time periods
  • get_wellness_data: Fetch wellness data
  • get_events: Retrieve upcoming events (workouts, races, etc.)
  • get_event_by_id: Get detailed information for a specific event
  • add_or_update_event: Create or update an event (workout, race, note, etc.)
  • delete_event: Delete a specific event
  • delete_events_by_date_range: Delete events within a date range
  • get_custom_items: Get custom items (charts, custom fields, zones, etc.) for an athlete
  • get_custom_item_by_id: Get detailed information for a specific custom item
  • create_custom_item: Create a new custom item for an athlete
  • update_custom_item: Update an existing custom item
  • delete_custom_item: Delete a custom item

Usage with ChatGPT

ChatGPT’s beta MCP connectors can also talk to this server over the SSE transport.

  1. Start the server in SSE mode so it exposes the /sse and /messages/ endpoints:

    bash
    export FASTMCP_HOST=127.0.0.1 FASTMCP_PORT=8765 MCP_TRANSPORT=sse FASTMCP_LOG_LEVEL=INFO
    python src/intervals_mcp_server/server.py

    The startup log prints the full URLs (for example http://127.0.0.1:8765/sse). ChatGPT needs that public URL, so forward the port with a tool such as ngrok http 8765 if you are not exposing the server directly.

  2. In ChatGPT, open Settings → Features → Custom MCP Connectors and click Add. Fill in:

    • Name: Intervals.icu
    • MCP Server URL: https://<your-public-host>/sse
    • Authentication: leave as No authentication unless you have protected your tunnel.

    You can reuse the same ngrok http 8765 tunnel URL here; just ensure it forwards to the host/port you exported above.

  3. Save the connector and open a new chat. ChatGPT will keep the SSE connection open and POST follow-up requests to the /messages/ endpoint announced by the server. If you restart the MCP server or tunnel, rerun the SSE command and update the connector URL if it changes.

Development and testing

Install development dependencies and run the test suite with:

bash
uv sync --all-extras
pytest -v tests

Running the server locally

To start the server manually (useful when developing or testing), run:

bash
mcp run src/intervals_mcp_server/server.py

Enabling debug logging

To capture server logs for debugging, wrap the command in a shell and redirect stderr to a file.

macOS/Linux — modify your claude_desktop_config.json like this:

json
{
  "mcpServers": {
    "Intervals.icu": {
      "command": "/bin/bash",
      "args": [
        "-c",
        "/Users/<USERNAME>/.local/bin/uv run --with 'mcp[cli]' --with-editable /path/to/intervals-mcp-server mcp run /path/to/intervals-mcp-server/src/intervals_mcp_server/server.py 2>> /path/to/intervals-mcp-server/mcp-server.log"
      ],
      "env": {
        "INTERVALS_API_BASE_URL": "https://intervals.icu/api/v1",
        "ATHLETE_ID": "<YOUR_ATHLETE_ID>",
        "API_KEY": "<YOUR_API_KEY>",
        "LOG_LEVEL": "INFO"
      }
    }
  }
}

Then tail the log file to see output in real-time:

bash
tail -f /path/to/intervals-mcp-server/mcp-server.log

Windows — modify your claude_desktop_config.json like this:

json
{
  "mcpServers": {
    "Intervals.icu": {
      "command": "powershell",
      "args": [
        "-Command",
        "C:\\Users\\<USERNAME>\\.local\\bin\\uv.exe run --with 'mcp[cli]' --with-editable C:\\path\\to\\intervals-mcp-server mcp run C:\\path\\to\\intervals-mcp-server\\src\\intervals_mcp_server\\server.py 2>> C:\\path\\to\\intervals-mcp-server\\mcp-server.log"
      ],
      "env": {
        "INTERVALS_API_BASE_URL": "https://intervals.icu/api/v1",
        "ATHLETE_ID": "<YOUR_ATHLETE_ID>",
        "API_KEY": "<YOUR_API_KEY>",
        "LOG_LEVEL": "INFO"
      }
    }
  }
}

Then monitor the log file in real-time using PowerShell:

powershell
Get-Content C:\path\to\intervals-mcp-server\mcp-server.log -Wait

License

The GNU General Public License v3.0

Featured

Glama.ai

Installation

TypingMind
Prerequisites:

Node.js 18+

{
  "mcpServers": {
    "mvilanova-intervals-mcp-server": {
      "command": "",
      "args": []
    }
  }
}

Use Intervals.icu MCP 服务器 MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once Intervals.icu MCP 服务器 is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add Intervals.icu MCP 服务器 as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the Intervals.icu MCP 服务器 MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "mvilanova-intervals-mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "intervals-mcp-server"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the Intervals.icu MCP 服务器 MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the Intervals.icu MCP 服务器 MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use Intervals.icu MCP 服务器 to help me with this task?
Intervals.icu MCP 服务器
Sure. I read it.
Here is what I found using Intervals.icu MCP 服务器.

Frequently asked questions

What is the Intervals.icu MCP 服务器 MCP server used for?

Intervals.icu MCP 服务器 is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use Intervals.icu MCP 服务器 MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use Intervals.icu MCP 服务器 with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use Intervals.icu MCP 服务器 MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With Intervals.icu MCP 服务器 connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect Intervals.icu MCP 服务器 MCP to TypingMind?

Intervals.icu MCP 服务器 runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does Intervals.icu MCP 服务器 MCP provide in TypingMind?

Intervals.icu MCP 服务器 exposes MCP capabilities that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use Intervals.icu MCP 服务器 MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If Intervals.icu MCP 服务器 requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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