paddleocr
Reads text from scanned pages, photos and PDFs and returns it as structured Markdown or JSON, so you can pull tables, formulas and headers out of a document without retyping them. Covers many languages and works as input for AI tools.
Lets Claude read, summarize, and log your Hevy workouts, routines, exercises, and body measurements from the terminal or through an MCP connection. Useful for reviewing training history or planning sessions.
Claude Code
$ npx -y skills add chrisdoc/hevy-mcp --agent claude-code
Codex
$ npx skills add chrisdoc/hevy-mcp -a codex
Cursor
$ npx skills add chrisdoc/hevy-mcp -a cursor
Gemini CLI
$ npx skills add chrisdoc/hevy-mcp -a gemini-cli
GitHub Copilot
$ npx skills add chrisdoc/hevy-mcp -a github-copilot
OpenCode
$ npx skills add chrisdoc/hevy-mcp -a opencode
Windsurf
$ npx skills add chrisdoc/hevy-mcp -a windsurf
Talk to your Hevy workout data from Claude, Cursor, Codex, and other MCP clients.
Connect to the hosted MCP · Use the Hevy CLI · Watch the 18-second demo · Explore all 23 tools · Documentation
Prefer the terminal? The separate
@chrisdoc/hevy-cli
package reads workouts, routines, exercises, and body measurements directly
from the Hevy API, and can create or update those resources with explicit
confirmation. Deletion is not supported.
npm install -g @chrisdoc/hevy-cli
export HEVY_API_KEY=your-hevy-api-key
hevy workouts list --page-size 10
hevy summary --weeks 4
Add --json to any command for scripts and pipelines. The CLI is a standalone
Hevy API client, not an MCP wrapper. See
packages/cli/README.md for the full command
reference, pagination behavior, and exit codes.
hevy-mcp is an open-source Model Context Protocol (MCP)
server for the Hevy fitness and workout tracking
app. It lets AI assistants read, analyze, create, and update your Hevy workouts,
routines, exercise templates, and body measurements through authenticated Hevy
API requests.
The Node stdio/HTTP adapter and Cloudflare Worker share the same MCP tools. See deployment modes to choose a transport, or architecture for workspace ownership, runtime scopes, and supported embedding APIs.
A Hevy API key, available with Hevy PRO, is required.

In the demo, the assistant retrieves real Hevy data and answers a multi-part training question with evidence from the user's workout history.
npx, bunx,
or the official Docker image.Try asking:
Analyze my training over the last six weeks. Show workouts per week, my most frequently trained exercises, any obvious gaps or inconsistencies, and cite the workout evidence you used.
Find my push-day routine and show its exercises and sets.
Compare my recent body measurements with my training consistency.
Create a completed workout from my saved routine. Ask me for any missing set results before writing it to Hevy.
The repository includes a Claude plugin that connects to the hosted OAuth-enabled MCP endpoint without embedding a user's Hevy API key.
In Claude, open Settings → Connectors → Add custom connector and enter:
https://mcp.hevy-mcp.dev/mcp
Complete the OAuth flow and enter the Hevy API key when prompted. The same remote endpoint can be used by Claude Desktop and other clients that support remote MCP connectors.
The Claude plugin is defined by .claude-plugin/plugin.json
and .mcp.json. Install it from this public repository or from
the Claude Plugin Directory after publication. It adds the hosted Hevy MCP
connector and the Hevy workout skill.
See the privacy policy for the hosted service's data handling details.
Create an API key in Hevy's API settings, then keep it somewhere secure. API access currently requires a Hevy PRO subscription.
hevy-mcp to your clientThe hosted Cloudflare endpoint is the fastest way to start. It runs remotely, so your client does not need Node.js, Bun, Docker, or a local server process.
Production URL:
https://mcp.hevy-mcp.dev/mcp
The endpoint uses Streamable HTTP. Send your Hevy API key as a bearer token on every request.
Codex CLI, the Codex desktop app, and the IDE extension share the same MCP configuration. Make your Hevy API key available in the environment that starts Codex, then add the hosted server:
export HEVY_API_KEY=your-hevy-api-key
codex mcp add hevy \
--url https://mcp.hevy-mcp.dev/mcp \
--bearer-token-env-var HEVY_API_KEY
Codex stores the environment variable name, not the key itself, in its MCP
configuration. Restart Codex or begin a new session, then run codex mcp list
to verify the server is configured.
Clients that accept a remote MCP URL and fixed headers commonly use this shape:
{
"mcpServers": {
"hevy": {
"url": "https://mcp.hevy-mcp.dev/mcp",
"headers": {
"Authorization": "Bearer your-hevy-api-key"
}
}
}
}
Exact configuration keys vary by client. The hosted server requires support for
Streamable HTTP and a fixed Authorization header.
Important Treat the bearer value like a password. The Worker validates it with Hevy for each request, does not store it, and forwards it to Hevy only as the required
api-keyheader.
Choose local stdio if you prefer to run the server on your own machine or your client cannot attach a fixed authorization header to remote MCP requests.
codex mcp add hevy \
--env HEVY_API_KEY=your-hevy-api-key \
-- npx -y hevy-mcp
Add this mcpServers entry to your client configuration:
{
"mcpServers": {
"hevy": {
"command": "npx",
"args": ["-y", "hevy-mcp"],
"env": {
"HEVY_API_KEY": "your-hevy-api-key"
}
}
}
}
There are two ways to configure the Hevy MCP server for Google Antigravity (agy):
This utilizes the built-in plugin system:
The plugin reads the repository-root mcp_config.json to start the local
stdio server. That configuration intentionally contains no API key; the server
inherits HEVY_API_KEY from the host environment as described below. This is
separate from the manual, user-global configuration described below and from
the repository's .mcp.json, which configures hosted HTTP/OAuth.
Install the plugin:
agy plugin install https://github.com/chrisdoc/hevy-mcp
Provide the HEVY_API_KEY in your host shell environment so the CLI child process can inherit it:
HEVY_API_KEY in your system/shell configurations:
~/.zshrc or ~/.bashrc):
export HEVY_API_KEY="your-actual-api-key"
[Environment]::SetEnvironmentVariable("HEVY_API_KEY", "your-actual-api-key", "User")
agy:
export HEVY_API_KEY="your-actual-api-key"
If you prefer configuring it statically via the global configuration file:
Open your global MCP configuration file:
~/.gemini/config/mcp_config.jsonAdd the hevy configuration block under the mcpServers key. Make sure to merge this entry with any existing servers you have configured rather than replacing the entire file contents:
{
"mcpServers": {
"hevy": {
"command": "npx",
"args": ["-y", "hevy-mcp"],
"env": {
"HEVY_API_KEY": "your-actual-api-key"
}
}
}
}
Common local configuration locations:
~/Library/Application Support/Claude/claude_desktop_config.json%APPDATA%\Claude\claude_desktop_config.json~/.cursor/mcp.jsonRestart or reconnect the client after saving the file.
Configure your client to launch this command with HEVY_API_KEY in the child
process environment:
npx -y hevy-mcp
npx requires Node.js 20 or newer. Restart or reconnect your client after
saving its configuration.
Use bunx instead
Requires Bun:
{
"mcpServers": {
"hevy": {
"command": "bunx",
"args": ["hevy-mcp@latest"],
"env": {
"HEVY_API_KEY": "your-hevy-api-key"
}
}
}
}
Use Docker instead
Official images support linux/amd64 and linux/arm64. Keep stdin open with
-i because the container runs the stdio MCP server:
export HEVY_API_KEY=your-hevy-api-key
docker run -i --rm -e HEVY_API_KEY ghcr.io/chrisdoc/hevy-mcp:latest
For an MCP client, store the key in a protected environment file and configure the client to launch Docker:
{
"mcpServers": {
"hevy": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"--env-file",
"/absolute/path/to/hevy-mcp.env",
"ghcr.io/chrisdoc/hevy-mcp:latest"
]
}
}
}
Pin an exact image tag such as ghcr.io/chrisdoc/hevy-mcp:X.Y.Z when you need
reproducible upgrades.
You can also add the npm server to supported clients with
add-mcp:
npx add-mcp hevy-mcp --env "HEVY_API_KEY=your-hevy-api-key"
Try one of these after restarting or reconnecting your MCP client:
Reads text from scanned pages, photos and PDFs and returns it as structured Markdown or JSON, so you can pull tables, formulas and headers out of a document without retyping them. Covers many languages and works as input for AI tools.
Gives your AI assistant a memory that persists across sessions, so it remembers your preferences and earlier work in this repository instead of starting fresh. Search, forget, pause and resume memories with simple commands.
Gives AI agents a library of ready-made skills for scientific work such as metabolic flux analysis, protein experiment design, time series modeling, and analytical method validation, so research tasks need less hand-built code. It works with any agent that supports the open Agent Skills standard.
Gives Claude Code skills, agents, and commands for PostHog analytics, session replay, feature flags, error tracking, and LLM observability. Includes MCP server setup and project access checks.
Install it with the command on its page. It includes hevy-cli, hevy-workouts. Its skills do not fire on their own yet. Free and open source.
Is this plugin yours?
Claim it with GitHubSubmit a pluginPromote it