📡 MCP Radar

clementrx/Performance-agent

Open-source AI Strength & Conditioning coach for your terminal. Runs in Claude Code / Gemini CLI / Codex — the LLM narrates, a deterministic engine calculates, and it won't lie about unrealistic goals

⭐ 3 stars
⑂ 0 forks
Python
momentum ▲ 6.0
created 2026-07-10
on radar since 2026-07-13
ai-agentclaude-codecoachinghyroxllmmcpmodel-context-protocolpythonrunningsports-sciencestrength-training
View on GitHub ↗Homepage ↗

About Performance-agent

🏋️ The first open-source AI Strength & Conditioning Coach powered by scientific research.

English · Français · Español · Deutsch · Italiano

It runs inside an AI agent CLI — a terminal program you chat with, such as Claude Code, Gemini CLI, or Codex — and turns it into a professional coach that designs, explains, monitors, and adapts your training. No backend, no API key, no hosting, no extra subscription. And it will tell you the truth when your goal is unrealistic:

Why another AI fitness coach? Because this one can't lie to you

LLM fitness coaches have two failure modes: they invent scientific references, and they tell you what you want to hear. PerformanceAgent is architected so neither is possible:

- LLMs narrate, the engine calculates. Every number — feasibility probabilities, race predictions, training loads, periodization waves — comes from a deterministic, property-tested Python engine. The agent explains the math; it never does the math. - Citations can't be hallucinated. The coach may only cite studies returned by the local evidence corpus (graded, DOI/PMID-verified). The PDF renderer hard-fails on any reference that isn't in the corpus. - Your data is files, not a cloud. Profile, programs, session logs, and check-ins live in a plain directory of markdown/YAML you can read, edit, diff, and sync.

PerformanceAgent isn't an app you open — it plugs into an AI agent CLI. Once plugged in, you just talk to it in plain language; no config files, no commands to memorize.

From the project README.

Maintaining this server?

Add the radar badge to your README — it shows your project was picked up by MCP Radar and links to this page:

[![On MCP Radar](https://mcp.liqiwa.com/badge.svg)](https://mcp.liqiwa.com/s/clementrx--Performance-agent.html)

Related MCP servers

bybit-exchange/kaas

Turn scattered notes, docs and transcripts into a queryable Markdown wiki — an LLM knowledge-base compiler with MCP access, no embeddings, self-hosted.

⭐ 82Python
PyModel/jev-judge-mcp

Typed judgment tools for MCP agents. TypeSafe's Jev model as verify, screen, find, classify, rerank, decide, compare, extract, review, gate, and score: the model judges, policy decides auto, review, o

⭐ 29Python
ismailsaoulaj/reddit-mcp-server

A production-grade Model Context Protocol (MCP) server. Empowers AI assistants (Claude, Cursor) to search, fetch, and analyze Reddit discussions with resilient rate-limiting and smart LLM noise filter

⭐ 22Python
Lemon177777/mcd-omni-assistant

麦当劳 MCP 全能助手 | 全覆盖 35 个工具 · 7 大工作流 · 14 条防错铁律 | 领券 / 点餐 / 外送 / 派对预约 / 积分到期止损,附零依赖 CLI 通道

⭐ 21Python
jiawei686/jev-ultrafast-mcp

Hand a whole browser task off in one call: a decision model drives the page server-side, so a flow costs one call, not a turn per click. Ref-based element tables, code-checked assertions, zero-model m

⭐ 18Python
00200200/usagetrim

Keep the signal. Cut the noise. Local CLI + MCP — fold verbose tool output for Claude Code, Codex, Cursor & Desktop.

⭐ 11Python

📬 Get the weekly radar in your inbox

The top new MCP servers of the week, every Monday. No spam, unsubscribe anytime.