{"id":"trymightyai-mighty-security","name":"mighty-security","homepage":null,"repo_url":"https://github.com/TryMightyAI/mighty-security","category":"security","subcategories":[],"tags":["mcp","security","sast","static-analysis","llm-analysis","dashboard","python","cli"],"what_it_does":"CLI/tooling to scan and analyze MCP servers for potentially malicious behavior (e.g., command injection, SSRF, credential/env leakage, path traversal). Includes an optional LLM mode (Cerebras) and mentions a web dashboard for monitoring/scans and exporting reports.","use_cases":["Pre-install / pre-integration security scanning of MCP servers","CI checks for MCP server code or repositories (non-zero exit on findings)","Risk triage and threat reporting for third-party MCP tooling","Runtime monitoring/proxy monitoring to catch suspicious behavior (basic, per README)"],"not_for":["Running MCP servers in production without a sandbox and additional controls","Auditing MCP server behavior at runtime without isolation (tooling may still expose sensitive data if misconfigured)","Compliance-grade assurance (no evidence of formal attestations, pen test reports, or audited guarantees)"],"best_when":"You are evaluating third-party MCP servers you did not author, and you can run the scanner in an isolated environment (and optionally enable LLM analysis) before installation/deployment.","avoid_when":"You need strong guarantees of zero false positives or zero missed issues, or you cannot afford the risk of scanning untrusted code in an environment with any sensitive credentials/files.","alternatives":["General SAST/DAST tools for code and dependencies (e.g., Semgrep, Bandit, Semgrep Cloud, CodeQL)","Containerized sandboxes for executing untrusted MCP handlers/tool calls","SBOM + dependency vulnerability scanning (e.g., Dependabot, Snyk, osv-scanner)","Custom policy checks tailored to MCP tool invocation patterns"],"af_score":41.5,"security_score":38.2,"reliability_score":28.8,"package_type":"mcp_server","discovery_source":["github"],"priority":"high","status":"evaluated","version_evaluated":null,"last_evaluated":"2026-03-30T13:46:43.164952+00:00","interface":{"has_rest_api":true,"has_graphql":false,"has_grpc":false,"has_mcp_server":false,"mcp_server_url":null,"has_sdk":false,"sdk_languages":[],"openapi_spec_url":null,"webhooks":false},"auth":{"methods":[],"oauth":false,"scopes":false,"notes":"No authentication mechanism described for accessing an API service. LLM mode requires providing a Cerebras API key (per README), implying use of external credentials, but no auth scheme for the scanner itself is documented."},"pricing":{"model":null,"free_tier_exists":false,"free_tier_limits":null,"paid_tiers":[],"requires_credit_card":false,"estimated_workload_costs":null,"notes":"No pricing information provided."},"requirements":{"requires_signup":false,"requires_credit_card":false,"domain_verification":false,"data_residency":[],"compliance":[],"min_contract":null},"agent_readiness":{"af_score":41.5,"security_score":38.2,"reliability_score":28.8,"mcp_server_quality":0.0,"documentation_accuracy":35.0,"error_message_quality":0.0,"error_message_notes":null,"auth_complexity":90.0,"rate_limit_clarity":35.0,"tls_enforcement":70.0,"auth_strength":20.0,"scope_granularity":20.0,"dependency_hygiene":55.0,"secret_handling":35.0,"security_notes":"Strengths (from README): mentions safe error handling (no info disclosure), URL/domain whitelisting, input validation/sanitization, rate limiting, and security headers for the dashboard. Risks/unknowns: no concrete evidence of TLS/auth for any remote service; optional LLM mode implies transmitting scan data to an external provider; no explicit guarantees about not logging secrets or handling sensitive inputs safely. Dependency hygiene cannot be verified from provided content; the dependency list is substantial and includes web/async frameworks, so supply-chain review is recommended.","uptime_documented":0.0,"version_stability":40.0,"breaking_changes_history":30.0,"error_recovery":45.0,"idempotency_support":"false","idempotency_notes":null,"pagination_style":"none","retry_guidance_documented":false,"known_agent_gotchas":["As a scanner, it may perform operations that can be risky with untrusted input; agents should run it in a sandbox and avoid sending sensitive data.","README suggests optional LLM analysis and a dashboard, but no stable programmatic API contract (schemas, endpoints, structured outputs) is described for agent integration.","Rate limiting is mentioned for local/GitHub scans, but there’s no documentation of headers, error codes, or retry guidance for API/automation flows."]}}