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 microsoft · MCP Server · ★ 155
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is mcp-interviewer safe to install? View the security audit →
MCP Interviewer A Python CLI tool that helps you catch MCP server issues before your agents do. PyPi Example Table of Contents How it works 🔎 Constraint checking 🛠️ Functional testing 🤖 LLM evaluation 📋 Report generation Quick Start Installation Example Usage CLI Bring Your Own Models Python Limitations Contributing Trademarks How it works 🔎 Constraint checking The MCP Interviewer helps avoid violating providers' hard constraints and warns you when you're not following recommended guidance. For example, OpenAI does not allow more than 128 tools in a single chat completion request, and recommends at most 20 tools. View all supported constraints Use to customize output. OpenAI name
| Stars | 155 |
| Forks | 20 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 73.779483406893/100 |
| Open Issues | 17 |
| Last Updated | 2026-09-14 |
| Created | 2025-07-09 |
| Platforms | mcp, python |
| Est. Tokens | ~16k |
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mcp-interviewer is Catch MCP server issues before your agents do.. It is categorized as a MCP Server with 155 GitHub stars.
mcp-interviewer is primarily written in Python. It covers topics such as agentic-ai, evaluation, linter.
You can find installation instructions and usage details in the mcp-interviewer GitHub repository at github.com/microsoft/mcp-interviewer. The project has 155 stars and 20 forks, indicating an active community.
mcp-interviewer is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to mcp-interviewer on Agent Skills Hub include eval-view, wren-engine, concierge. 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: