Hevy MCP Server

Talk to your Hevy workout data from Claude, Cursor, Codex, and other MCP clients.

Connect to the hosted MCP · Watch the 18-second demo · Explore all 25 tools

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.

A Hevy API key, available with Hevy PRO, is required.

See it in action

In the demo, the assistant retrieves real Hevy data and answers a multi-part training question with evidence from the user's workout history.

Related MCP server: hevy-mcp

What can you do with it?

• Analyze training progress: summarize 1-12 weeks of workouts and body measurements in one tool call.

• Ask questions in plain language: find recent sessions, frequently trained exercises, consistency gaps, routine details, or exercise history.

• Plan and log training: create or update workouts, routines, routine folders, custom exercises, and body measurements.

• Search without huge responses: discover routines and exercise templates with compact, AI-friendly results.

• Connect from your preferred MCP client: use the hosted Streamable HTTP endpoint or run locally with Codex, Claude Desktop, Cursor, and other clients.

• Start without installing anything: connect directly to the production Cloudflare Worker—no Node.js, package download, or Docker container required.

• Keep local control when you want it: run the same server with 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.

Quick start

1. Get your Hevy API key

Create an API key in Hevy, then keep it somewhere secure. API access currently requires a Hevy PRO subscription.

2. Connect hevy-mcp to your client

The 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.

Connect to the hosted endpoint

Production URL:

https://hevy.chrisdoc.dev/mcp

The endpoint uses Streamable HTTP. Send your Hevy API key as a bearer token on every request.

Codex

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://hevy.chrisdoc.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.

Other Streamable HTTP clients

Clients that accept a remote MCP URL and fixed headers commonly use this shape:

{
	"mcpServers": {
		"hevy": {
			"url": "https://hevy.chrisdoc.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.

Run locally instead

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

codex mcp add hevy \
  --env HEVY_API_KEY=your-hevy-api-key \
  -- npx -y hevy-mcp

Claude Desktop or Cursor

Add this mcpServers entry to your client configuration:

{
	"mcpServers": {
		"hevy": {
			"command": "npx",
			"args": ["-y", "hevy-mcp"],
			"env": {
				"HEVY_API_KEY": "your-hevy-api-key"
			}
		}
	}
}

Common local configuration locations:

• Claude Desktop on macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

• Claude Desktop on Windows: %APPDATA%\Claude\claude_desktop_config.json

• Cursor: ~/.cursor/mcp.json

Restart or reconnect the client after saving the file.

Any stdio MCP client

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.

Requires Bun:

{
	"mcpServers": {
		"hevy": {
			"command": "bunx",
			"args": ["hevy-mcp@latest"],
			"env": {
				"HEVY_API_KEY": "your-hevy-api-key"
			}
		}
	}
}

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"

3. Ask your first question

Try one of these after restarting or reconnecting your MCP client:

• “Give me a training summary for the last four weeks.”

• “What routines do I have saved on Hevy?”

• “Show my three most recent workouts.”

• “Find exercise templates containing squat.”

• “Which Hevy account is connected?”

Your assistant should ask for approval before mutation tools when the client supports tool confirmations.

How it works

Hosted:  Your AI assistant  →  Streamable HTTP  →  Cloudflare Worker  →  Hevy API
Local:   Your AI assistant  →  MCP over stdio   →  local hevy-mcp     →  Hevy API

The hosted endpoint creates a fresh MCP server and Hevy client for each request. It validates the supplied key with Hevy, keeps no shared user session, and does not persist the key. The local server follows the same tool contract but runs on your machine and receives the key through its child-process environment.

In either mode, read tools retrieve data; mutation tools create or replace data only when your assistant calls them.

Guided prompts

These server-provided MCP prompts coordinate common multi-step workflows:

Tools

hevy-mcp registers 25 tools. Read-only tools are safe for exploration; create and update tools are exposed with MCP mutation annotations so compatible clients can request confirmation.

The Hevy API currently exposes no delete endpoints for workouts, routines, routine folders, exercise templates, or body measurements, so there are no corresponding delete tools.

Resources

Hosted Cloudflare endpoint

The production MCP server is live at:

https://hevy.chrisdoc.dev/mcp

It is the quickest way to use hevy-mcp: there is nothing to install or keep running locally, and it exposes the same 25 tools as the npm package and Docker image.

The Cloudflare Worker uses stateless Streamable HTTP at POST /mcp. Clients must send their Hevy API key as a fixed authorization header:

{
	"mcpServers": {
		"hevy": {
			"url": "https://hevy.chrisdoc.dev/mcp",
			"headers": {
				"Authorization": "Bearer your-hevy-api-key"
			}
		}
	}
}

The bearer value is your Hevy API key, not an OAuth token. The Worker validates the key with Hevy on each request, does not store it, and forwards it upstream only as Hevy's required api-key header.

The endpoint does not expose legacy SSE or a GET event stream. Clients that require OAuth discovery, dynamic registration, token refresh, or legacy SSE are not compatible unless they can send the fixed custom header above.

Self-host the Worker

See CONTRIBUTING.md to deploy the Cloudflare Worker for self-hosted Streamable HTTP.

Advanced configuration

The local executable is stdio-only. It does not support PORT, HEVY_MCP_TRANSPORT, or --transport, and it does not provide local HTTP or SSE behavior.

Cache behavior

search-exercise-templates and hevy://exercise-templates share a server-scoped in-memory catalog cache:

• Entries live for five minutes, and the cache holds at most one catalog.

• Concurrent catalog requests share an in-flight fetch when possible.

• search-exercise-templates accepts refresh: true to invalidate the cache.

• Paginated get-exercise-templates calls always fetch their requested page.

• Each hosted Worker request gets a fresh cache, preventing cross-key sharing.

Security and mutations

• Keep HEVY_API_KEY out of source control, URLs, logs, and screenshots.

• Local clients provide the key through the child process environment.

• Hosted clients send the key only in the Authorization: Bearer header. The Worker validates each key with Hevy, does not store it, and sends it upstream only as Hevy's api-key header.

• Browser requests to a self-hosted Worker must exactly match an origin in MCP_ALLOWED_ORIGINS; wildcard CORS is intentionally unsupported.

• Create operations can produce duplicates when retried. Update operations replace existing records. Review tool inputs and use client confirmations.

Troubleshooting

• The server does not appear: restart or reconnect your MCP client after changing its configuration.

• npx fails: confirm that Node.js 20 or newer is installed, then run npx -y hevy-mcp --version in a terminal.

• Codex cannot see the server: run codex mcp list, then start a new Codex session after confirming the hevy entry exists.

• Hosted authentication fails: confirm the key is active, belongs to a Hevy PRO account, and is sent as Authorization: Bearer <HEVY_API_KEY>.

• Local authentication fails: confirm the key is active and available to the MCP child process as HEVY_API_KEY.

• Need diagnostics: set HEVY_MCP_DEBUG=1. Diagnostic output goes to stderr and does not interfere with MCP messages on stdout.

If you find a bug or have a feature request, open an issue.

Contributing

Contributions are welcome. Developer setup, testing lanes, generated-client workflows, Cloudflare Worker deployment, and pull request rules are documented in CONTRIBUTING.md.

License and acknowledgements

• License: MIT

• Credits: Model Context Protocol and Hevy Fitness