hieuchaydi

hieuchaydi / MCPGuard

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MCPGuard is a CLI for security and reliability checks on MCP tools before AI agents call them. It validates schema quality, runtime behavior, timeout handling, and secret leakage risks.

10
1
100% credibility
Found May 09, 2026 at 10 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

MCPGuard tests AI agent tools for security risks including secret leaks, timeouts, permission issues, and prompt injections before use.

How It Works

1
🔍 Discover MCPGuard

You hear about MCPGuard, a helpful checker that tests AI tools for safety before your smart assistants use them.

2
📥 Get it ready

You easily prepare MCPGuard on your computer so it's all set to start protecting your tools.

3
🛡️ Set your safety wishes

You describe the kinds of dangers to watch for, like slow tools or hidden tricks, in simple rules.

4
🚀 Test your AI tool

You tell MCPGuard about one of your AI tools, and it quickly runs safety checks to spot any risks.

5
📊 Review the colorful report

You get a friendly summary showing what passed, what needs fixing, and tips to make it safer.

✅ Tools are safe to use

With risks fixed or confirmed low, your AI assistants can now trust and call those tools confidently.

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Star Growth

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AI-Generated Review

What is MCPGuard?

MCPGuard is a Python CLI tool that runs security and reliability checks on MCP tools before AI agents call them. It scans for schema quality issues, runtime behavior problems, timeout handling failures, and secret leakage risks, acting as a trust gate to catch unsafe tools early. Developers get configurable policies, JSON reports, and fail thresholds via simple commands like `mcpguard mcp test basic-demo --fail-on high`.

Why is it gaining traction?

Unlike basic schema validators, MCPGuard probes real runtime behavior with fuzzing, permission boundaries, and prompt injection detection, plus secret scans and timeout tests. Its CLI supports server registries, CI workflows, and Docker for quick scans, making it dead simple to gate MCP tools in pipelines. The risk-scored reports and vulnerable demos hook devs tired of agents crashing on leaky or slow tools.

Who should use this?

AI agent builders integrating MCP servers need it to validate tools before production calls. MCP tool authors use it for pre-deploy checks on path access, output safety, and error handling. Teams in CI/CD pipelines enforcing reliability will slot it in easily for high-risk agent workflows.

Verdict

Try MCPGuard if you're building agentic systems—its checks fill a real gap, with strong docs, examples, and CLI polish despite 10 stars and 1.0% credibility signaling early maturity. Watch for pending side-effect detection; for now, it's a lightweight Python win for MCP guard on GitHub.

(198 words)

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