No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by alpacahq · MCP Server · ★ 962
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is alpaca-mcp-server safe to install? View the security audit →
A comprehensive Model Context Protocol (MCP) server for Alpaca's Trading API. Enable natural language trading operations through AI assistants like Claude, Cursor, and VS Code. Supports stocks, options, crypto
| Stars | 962 |
| Forks | 312 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 72.3355099300708/100 |
| Open Issues | 24 |
| Last Updated | 2026-09-15 |
| Created | 2025-06-03 |
| Platforms | mcp, python |
| Est. Tokens | ~25k |
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alpaca-mcp-server is Alpaca’s official MCP Server lets you trade stocks, ETFs, crypto, and options, run data analysis, and build strategies in plain English directly from your favorite LLM tools and IDEs. It is categorized as a MCP Server with 962 GitHub stars.
alpaca-mcp-server is primarily written in Python. It covers topics such as ai-trading, algorithmic-trading, crypto.
You can find installation instructions and usage details in the alpaca-mcp-server GitHub repository at github.com/alpacahq/alpaca-mcp-server. The project has 962 stars and 312 forks, indicating an active community.
alpaca-mcp-server is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to alpaca-mcp-server on Agent Skills Hub include open-trading-api, maverick-mcp, tradememory-protocol. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: