Kubernetes deployment intelligence — typed query tools + AI-synthesised risk briefs. CLI + MCP server.
Project description
mcp-deploy-intel
Kubernetes deployment intelligence — typed query tools + AI-synthesised risk briefs. CLI + MCP server.
Status: v0.6.0 — 9 tools live — MVP complete. Two K8s-only tools + three Prometheus tools + three SQLite deploy-history tools + K8s informer loop + async scoring loop + AI-synthesised generate_risk_brief (LLM via instructor, deterministic offline fallback). v1.0.0 adds Helm Deployment mode + cluster matrix CI.
Install
# Run ephemerally via uvx (recommended for one-off queries):
uvx mcp-deploy-intel --help
# Persistent:
pip install mcp-deploy-intel
Quickstart
# List Deployments + StatefulSets in a namespace
deploy-intel list-workloads-in-namespace default
# Snapshot a single workload
deploy-intel get-workload default/Deployment/my-api --format md
# Watch cluster deployments — auto-record every new rollout to SQLite
deploy-intel watch # all namespaces
deploy-intel watch --namespace prod --namespace staging # scoped
# Score pending deploys — writes ROLLED_BACK / DEGRADED / SUCCESS / UNKNOWN
deploy-intel evaluate # defaults: window=600s, interval=60s
deploy-intel evaluate --window-s 300 --interval-s 30 --prom-url http://prometheus:9090
MCP — Claude Desktop / Claude Code
{
"mcpServers": {
"deploy-intel": {
"command": "uvx",
"args": ["mcp-deploy-intel", "serve-mcp"]
}
}
}
Current tools exposed via MCP (8 total): get_workload, list_workloads_in_namespace, get_metric_trend, get_inbound_traffic, get_outbound_traffic, record_deploy, get_deploy_history, get_rollback_history.
Design & plans
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