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Description
Context
Guardrails AI does excellent work on LLM output validation. With MCP (Model Context Protocol) becoming the standard for connecting AI agents to tools, there's a new compliance surface: the MCP server itself.
The Gap
Current guardrails focus on LLM inputs/outputs. But when an agent calls an MCP server, the security of that server matters too:
- Is the tool definition signed and tamper-proof? (MCP-03)
- Can tools be redefined after trust? (MCP-04)
- Are JSON-RPC messages authenticated? (MCP-06)
- Is there an audit trail? (MCP-08)
mcps-audit -- OWASP Compliance Scanner for MCP Servers
We built a scanner that checks MCP servers against OWASP standards:
npx mcps-audit ./your-mcp-server- OWASP MCP Top 10: 10 protocol-level risks with PASS/WARN/FAIL status
- OWASP Agentic AI Top 10: 12 code-level security rules
- PDF compliance report with findings, line numbers, remediation
This could be a valuable "pre-deployment guardrail" -- scan the MCP server before connecting it to your agent pipeline.
Links
- npm: https://www.npmjs.com/package/mcps-audit
- GitHub: https://github.com/razashariff/mcps-audit
- OWASP MCP Top 10: https://owasp.org/www-project-mcp-top-10/
- Sample Report: https://agentsign.dev/sample-report.pdf
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