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 JohannLai · LLM Plugin · ★ 310
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is openai-function-calling-tools safe to install? View the security audit →
OpenAI Function calling tools OpenAI Function calling tools OpenAI Function calling tools is a repository that offers a set of tools to help you easy to build a function calling model with OpenAI API. More information about function calling Sample: https://chatFn.io 🪓 Tools The repo provides the following tools you can use out of the box: 🗺️ ShowPoisOnMap: A tool that can show points of interest on a map. 🌐 ReverseGeocode: A tool that can convert coor
| Stars | 310 |
| Forks | 29 |
| Language | TypeScript |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 68.2041472926808/100 |
| Open Issues | 4 |
| Last Updated | 2024-01-30 |
| Created | 2023-06-16 |
| Platforms | node |
| Est. Tokens | ~29k |
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openai-function-calling-tools is 🛠 openai function calling tools for JS/TS. It is categorized as a LLM Plugin with 310 GitHub stars.
openai-function-calling-tools is primarily written in TypeScript. It covers topics such as function-calling, functions, javascript.
You can find installation instructions and usage details in the openai-function-calling-tools GitHub repository at github.com/JohannLai/openai-function-calling-tools. The project has 310 stars and 29 forks, indicating an active community.
openai-function-calling-tools is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to openai-function-calling-tools on Agent Skills Hub include openai-assistant-swarm, SirChatalot, awesome-ai-sdks. 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: