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 Chen-zexi · MCP Server · ★ 732
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is open-ptc-agent safe to install? View the security audit →
Open PTC Agent English | 中文 Getting Started Roadmap Demo: Analyzing 2 years of NVDA, AMD & SPY stock data (15,000+ lines of raw JSON) using DeepSeek V3.2 What is Programmatic Tool Calling? This project is an open source implementation of Anthropic recently introduced Programmatic Tool Calling (PTC), which enables agents to invoke tools with code execution rather than making individual JSON tool calls. This paradigm is also featured in their earlier engineering blog Code execution with MCP. Why PTC? LLMs are exceptionally good at writing code! They exce
| Stars | 732 |
| Forks | 90 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 72.6431433767523/100 |
| Open Issues | 4 |
| Last Updated | 2026-01-21 |
| Created | 2025-11-07 |
| Platforms | mcp, python |
| Est. Tokens | ~1977k |
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open-ptc-agent is An open source implementation of code execution with MCP (Programatic Tool Calling). It is categorized as a MCP Server with 732 GitHub stars.
open-ptc-agent is primarily written in Python. It covers topics such as agent, daytona, langchain.
You can find installation instructions and usage details in the open-ptc-agent GitHub repository at github.com/Chen-zexi/open-ptc-agent. The project has 732 stars and 90 forks, indicating an active community.
open-ptc-agent is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to open-ptc-agent on Agent Skills Hub include LangAlpha, flock, AgentChat. 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: