ironmanus-mcp
Verified Safeby dnnyngyen
Overview
Orchestrates AI workflows with an 8-phase control flow and specialized tools, serving as a Model Context Protocol (MCP) server.
Installation
docker-compose up -dEnvironment Variables
- KNOWLEDGE_MAX_CONCURRENCY
- KNOWLEDGE_TIMEOUT_MS
- KNOWLEDGE_CONFIDENCE_THRESHOLD
- KNOWLEDGE_MAX_RESPONSE_SIZE
- AUTO_CONNECTION_ENABLED
- RATE_LIMIT_REQUESTS_PER_MINUTE
- RATE_LIMIT_WINDOW_MS
- MAX_CONTENT_LENGTH
- MAX_BODY_LENGTH
- VERIFICATION_COMPLETION_THRESHOLD
- EXECUTION_SUCCESS_RATE_THRESHOLD
- INITIAL_REASONING_EFFECTIVENESS
- MIN_REASONING_EFFECTIVENESS
- MAX_REASONING_EFFECTIVENESS
- ALLOWED_HOSTS
- ENABLE_SSRF_PROTECTION
- USER_AGENT
- NODE_ENV
Security Notes
The project demonstrates strong, explicit, and multi-layered security measures. SSRF protection (`ssrfGuard`, `validateAndSanitizeURL`) is consistently applied to network requests and session IDs. Python execution is sandboxed (`validatePythonCode` blocks dangerous functions) and `pip install` uses an allowlist (`ALLOWED_LIBRARIES`). Path traversal is prevented for file system operations via `isValidSessionId`. An active runtime protection system (`startLegacyFileProtection`) removes legacy JSON files. Configuration validation (`validateConfig`) checks critical security settings in production. No obvious hardcoded secrets were found. The primary remaining risk is the inherent trust boundary of `subprocess.check_call` for `pip install` even with an allowlist, as a malicious package could potentially bypass checks if it made it into a mirror.
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