mcp-market-server
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
mcp-market-server
MCP (Model Context Protocol) server exposing live crypto market data and a paper-trading engine as standardized tool calls — so any agent (Claude Code, custom multi-agent systems) can query Bybit and execute simulated trades.
Built for the Tower Research Capital AI/ML intern application: demonstrates MCP integration, agent tool-calling, risk controls, and production-style Python.
Architecture
┌─────────────┐ MCP stdio ┌──────────────────┐ REST ┌─────────┐
│ Claude Code │ ◄───────────────► │ FastMCP Server │ ─────────► │ Bybit │
│ / Agent │ │ (tools.py) │ public API │ v5 │
└─────────────┘ └────────┬─────────┘ └─────────┘
│
┌────────▼─────────┐
│ Paper Engine │
│ SQLite + Risk │
└──────────────────┘Related MCP server: KirpyV3 MCP Server
Tools Exposed
Tool | Description |
| OHLCV candles for any symbol/timeframe |
| L2 order book snapshot |
| Latest price / 24h stats |
| Market/limit order into paper engine (risk-guarded) |
| Cancel an open paper order |
| Open positions + unrealized P&L |
| Open/filled order history |
| Flatten a position at market |
| Cash balance, equity, realized/unrealized P&L |
Risk Guards (paper engine)
Max notional per order (
risk.max_order_notional_usd)Max open positions (
risk.max_open_positions)Daily loss halt — trading disabled once daily drawdown exceeds cap
Every order + decision is written to an audit log table
Setup
cd mcp-market-server
python -m venv .venv && .venv\Scripts\activate # Windows
pip install -e ".[dev]"
cp .env.example .env # optional overrides
pytest # run testsRegister with Claude Code
{
"mcpServers": {
"market": {
"command": "python",
"args": ["-m", "mcp_market_server.server"],
"cwd": "/absolute/path/to/mcp-market-server"
}
}
}Or with the CLI:
claude mcp add market -- python -m mcp_market_server.serverDemo Prompts (after registering)
Fetch the last 100 hourly candles for BTCUSDT and summarize the trend.
Place a paper long of 0.01 BTCUSDT with a 2% stop loss and take profit at 3%.
Show my account, then flatten everything if unrealized PnL is worse than -$50.Design Notes
Public endpoints only — no API keys, no real money. Paper engine fills market orders against live ticker price, limit orders when price touches.
SQLite for orders/positions/trades/audit — zero-dependency persistence, mirrors how quant desks log every decision.
Risk layer sits between tools and engine — same pattern as a real desk: strategy proposes, risk disposes.
Tool Schema Changelog
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Maintenance
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