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title: Why Kubernetes Querying Is Broken and How I Fixed It
description: kubectl get all is misleading — it only retrieves a small subset of cluster resources, missing ingresses, custom resources, and much more. With 356+ resource...
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# Why Kubernetes Querying Is Broken and How I Fixed It

**[DevOps Toolkit](https://daily.dev/sources/devops-toolkit)** · 11 min read · 1 upvotes · 0 comments

## Summary

kubectl get all is misleading — it only retrieves a small subset of cluster resources, missing ingresses, custom resources, and much more. With 356+ resource definitions in a typical cluster, iterating over all APIs via bash loops is slow and error-prone. The solution proposed is syncing Kubernetes metadata into a vector database (Qdrant), enabling both traditional SQL-style queries and semantic search. This allows instant, intuitive queries like 'find all databases' that work regardless of naming conventions (DB, postgresql, psql, etc.). An MCP server exposes the data to AI agents like Claude Code or Cursor, which can query the vector DB and call kubectl for live data. The author also promotes an open-source project called DevOps AI Toolkit that implements this pattern.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.youtube.com/watch?v=uUdbQkq5c4k>

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- [Investigate Kubernetes resources with Datadog MCP tools](https://daily.dev/posts/investigate-kubernetes-resources-with-datadog-mcp-tools-oqbwd2hka) · Datadog · 5 upvotes · 0 comments

---

Tags: [#devops](https://daily.dev/tags/devops), [#kubernetes](https://daily.dev/tags/kubernetes), [#vector-search](https://daily.dev/tags/vector-search)

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