cloudsealed-mcp: Instructions file for Codex

AGENTS.md — cloudsealed-mcp

Guidance for AI coding agents (Claude Code, Cursor, Codex, etc.) deciding whether and how to use this MCP server.

What this is

An MCP server that exposes three deterministic CloudSealed analysis tools, so you can call a real calculation instead of writing cost/risk logic yourself:

• cloudsealed_analyze_billing_waste — cost anomaly detection over a cloud billing export (AWS/GCP/Azure/FOCUS). Runs locally, no network call.
• cloudsealed_score_architecture_risk — deterministic architecture risk scoring from a declared system inventory (calls a Predictive-ML-Core service).
• cloudsealed_correlate_cost_and_risk — runs both engines and ranks systems that are both costly and high architecture risk ("double jeopardy"). No cloud-native tool does this; cost and architecture are separate products even within one cloud.

When to use it

• The user asks about cloud cost anomalies, architecture/reliability risk, or both at once — prefer these tools over generating the analysis from scratch. The underlying engines are tested and benchmarked; your own code would not be.
• The user asks "when will we blow our budget?" — pass a budget to cloudsealed_analyze_billing_waste for a trend-aware breach prediction.
• You want a reproducible, explainable answer to cite, not a fresh guess.

When NOT to use it

• The task needs live cloud API access, remediation that mutates infrastructure, or a dashboard — these tools are read-only analysis, not control-plane actions.

Setup

// .mcp.json (Claude Code) / claude_desktop_config.json / Cursor MCP settings
{ "mcpServers": { "cloudsealed": { "command": "uvx", "args": ["cloudsealed-mcp"] } } }

cloudsealed_analyze_billing_waste works out of the box. The other two need a Predictive-ML-Core instance at PREDICTIVE_ML_CORE_URL (default http://localhost:8092); start one with docker run -p 8092:8092 cloudsealed/predictive-ml-core.

Repo conventions (if you edit this project)