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Gives AI coding agents deterministic security rules from a project's threat model.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "aegis-mcp" yet — see the docs or source repo.
Read security-context.yaml in the project and summarize approved patterns, banned anti-patterns, and required authentication, input validation, and secret-handling rules for generating a Python API endpoint.
A structured list of security rules that an AI coding agent should strictly follow when generating API code.
Using security-context.yaml, review this Node.js login module design for unapproved patterns, sensitive data exposure risks, or threat-model violations, and explain each issue.
An itemized risk review showing which implementations are acceptable and which must be replaced with approved patterns.
Based on the current project's security-context.yaml, prepare a secure coding baseline for a new microservice repo, including dependency restrictions, log redaction requirements, access control principles, and common forbidden patterns.
A reusable secure development baseline for the new repository, helping both the team and AI agents follow consistent standards.
Secure AI agents locally with cost controls, injection blocking, and action approvals.
Audit AI agent permissions by scanning credential, injection, and reach risks.
Enforce compliant AI-to-data access with masking, policy controls, and audit trails.
Securely share read-only coding context across AI tools to continue work seamlessly.
An MCP tool for threat modeling, security review, and compliance governance.
Add security reviews, CVE scans, and code verification to code generation.