Flagged: opens a tunnel service. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by XiyaoWang0519 · MCP Server · ★ 97
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is agent-call safe to install? View the security audit →
Agent Call Agent Call lets an AI assistant make phone calls on your behalf. Give it a task, review the call plan, and explicitly confirm before it dials. You can follow the call, provide answers, stop it, and retrieve the result and transcript. Developed with Codex, Agent Call uses OpenAI GPT-Live SIP () with a delegated task backend for the phone conversation and Twilio to reach the recipient. You run the service yourself, using your own OpenAI and Twilio accounts. Works with ChatGPT Work and Claude web, verified with real question-and-answer calls. It does not work in regular ChatGPT mode. See client setup, test results, and verification limits. What you need | OpenAI API
| Stars | 97 |
| Forks | 1 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 58.4560199024289/100 |
| Open Issues | 6 |
| Last Updated | 2026-09-15 |
| Created | 2026-07-13 |
| Platforms | mcp, python |
| Est. Tokens | ~13k |
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agent-call is Open-source MCP server for safe, stateful AI-agent phone calls with OpenAI Realtime and Twilio.. It is categorized as a MCP Server with 97 GitHub stars.
agent-call is primarily written in Python. It covers topics such as ai-agents, fastapi, mcp.
You can find installation instructions and usage details in the agent-call GitHub repository at github.com/XiyaoWang0519/agent-call. The project has 97 stars and 1 forks, indicating an active community.
agent-call is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to agent-call on Agent Skills Hub include OmoiOS, omnicoreagent, hermes-talk. 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: