aiTrainer
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@aiTrainerLog squat 5 reps at 100 kg"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
aiTrainer
Personal workout coach as a Python MCP server for OpenClaw. Chat over Telegram, log exercises in natural language, and let the agent read structured progress from SQLite.
Features
Log exercises, sets, reps, weights, optional RPE, and notes
Automatic workout session grouping (same day + within idle timeout)
Exercise aliases (
bench,bench press, etc.)Progress signals: estimated 1RM, personal bests, volume trend, sessions since last increase
MCP stdio transport for OpenClaw
Related MCP server: MCP Logger
Requirements
Python 3.11+
Linux target machine (also works on macOS for development)
Install
python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"Run locally
aicoach-mcpOr:
python -m aicoach.serverConfiguration
Environment variables:
Variable | Default | Description |
|
| SQLite database path |
|
| Default weight unit |
|
| Auto-close idle workout sessions |
OpenClaw setup
Add aiCoach to your OpenClaw MCP config. On a standard install this lives in ~/.openclaw/openclaw.json.
Option A: CLI helper
openclaw mcp set aicoach '{
"command": "/path/to/aicoach/.venv/bin/aicoach-mcp",
"env": {
"AICOACH_DB_PATH": "/home/you/.local/share/aicoach/aicoach.db"
}
}'Option B: direct JSON config
{
"mcpServers": {
"aicoach": {
"command": "/path/to/aicoach/.venv/bin/aicoach-mcp",
"args": [],
"env": {
"AICOACH_DB_PATH": "/home/you/.local/share/aicoach/aicoach.db"
}
}
}
}Notes:
A
commandfield means OpenClaw launches the server over stdio automatically.Use the absolute path to your virtualenv binary on the Linux host.
Restart or reload OpenClaw after changing MCP config.
Agent prompt
Copy prompts/coach_instructions.md into your OpenClaw agent instructions so the model knows when to call aiCoach tools.
MCP tools
Tool | Purpose |
| Log one exercise and attach it to the current session |
| Show the open session and exercises logged so far |
| Recent sessions for one exercise |
| Recent sessions across exercises |
| Coaching signals for one exercise |
| Known exercises and aliases |
Example tool input
{
"exercise": "squat",
"sets": [
{"reps": 5, "weight": 100},
{"reps": 5, "weight": 100},
{"reps": 5, "weight": 100}
],
"note": "moved well"
}Tests
pytestMCP stdio smoke test:
python scripts/mcp_smoke_test.pyOpenClaw example config
See examples/openclaw-mcp-snippet.json for a copy-paste MCP server entry.
Project layout
aicoach/
config.py # settings and env vars
db.py # sqlite schema
repository.py # storage and session logic
progress.py # coaching signals
server.py # MCP server
prompts/
coach_instructions.md
tests/Maintenance
Resources
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Looking for Admin?
If you are the server author, to access and configure the admin panel.
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