Linter for AI agent context files and MCP configs: CLAUDE.md, AGENTS.md, .cursorrules, .mcp.json - catches broken paths, wrong commands, leaked secrets

🛡️ Security audit CLI for Model Context Protocol (MCP) servers — scan AI agent configs for tool poisoning, rug pulls, hardcoded secrets, command injection & supply-chain risks. Pure Python, SARIF + CI ready.

Enables AI agents and assistants to inspect live system telemetry, check TCP port connectivity, audit environment variables for leaked secrets, analyze and summarize log files, and list Docker containers — all with token-efficient, compressed output.

Exposes an assess_repo tool that scans a repository, local folder, or npm/PyPI package and returns supply-chain risk findings with file, line, and code evidence — install-time scripts, dangerous patterns, secrets, AI-agent manipulation, impersonation and published-artifact mismatches — while never executing the target's code. Findings carry severities from info to critical so results can gate installs and CI jobs.

Lets Claude or any MCP client inspect a TP-Link Deco mesh locally — nodes, backhaul, clients, WAN, firmware — and carefully change settings with graded, read-back-verified writes, secrets redacted by default and an optional read-only mode. No cloud or app involved.

Local MCP server that gives coding agents a verifiable project memory, trims what enters their context, and masks secrets — with honest accounting of what it saves and costs.

Local MCP server that gives coding agents a verifiable project memory, trims what enters their context, and masks secrets — with honest accounting of what it saves and costs.

Search research and community sources. Queries logged for research; optional purpose. No secrets.

Find and redact leaked secrets, wallet keys and PII before an AI agent sends text.

Search, read, send and run API collections over MCP. Secrets masked; protected envs gated.