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 jdez427 · MCP Server · ★ 133
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is claude-ipc-mcp safe to install? View the security audit →
Claude IPC MCP - Let Your AI Agents Talk to Each Other Enable AI-to-AI communication using simple natural language commands. Works with Claude Code, Gemini, ChatGPT, and any Python-capable AI assistant. What It Does Claude IPC MCP lets different AI assistants send messages to each other, even across different platforms and sessions. Think of it as email for AIs - simple, reliable, and persistent. Quick Install (2 minutes) Full installation guide: INSTALL.md Key Features 💬 Natural language commands - No coding required 💾 Persistent messages - Messages survive restarts 🔄 Cross-platform - Works between different AI platforms 🎯 Sim
| Stars | 133 |
| Forks | 26 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 70.2044177059312/100 |
| Open Issues | 2 |
| Last Updated | 2025-09-25 |
| Created | 2025-07-07 |
| Platforms | claude-code, cli, gemini, mcp, python |
| Est. Tokens | ~17k |
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claude-ipc-mcp is AI-to-AI communication protocol for Claude, Gemini, and other AI assistants. It is categorized as a MCP Server with 133 GitHub stars.
claude-ipc-mcp is primarily written in Python. It covers topics such as ai-messaging, claude-code, communication-protocol.
You can find installation instructions and usage details in the claude-ipc-mcp GitHub repository at github.com/jdez427/claude-ipc-mcp. The project has 133 stars and 26 forks, indicating an active community.
claude-ipc-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to claude-ipc-mcp on Agent Skills Hub include sudocode, systemprompt-code-orchestrator, claude-talk-to-figma-mcp. 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: