MCP as a Judge acts as a validation layer between AI coding assistants and LLMs, helping ensure safer and higher-quality code.
From the registry: MCP as a Judge: a behavioral MCP that strengthens AI coding assistants via explicit LLM evaluations

Please install the `mcp-as-a-judge` MCP server into my current AI client (that's you).
Required prerequisites (do these first if not already done):
- **Docker** — Docker must be installed
- **MCP client with sampling+elicitation** — Client must support MCP Sampling and Elicitation features
Canonical MCP server config (stdio transport):
- command: `docker`
- args: ["run","--rm","-i","--pull=always","ghcr.io/othervibes/mcp-as-a-judge:latest"]
- optional environment variables:
- `LLM_API_KEY`: LLM API key (optional with GitHub Copilot + VS Code which provides MCP sampling) (example: `<your-api-key>`)
- `LLM_MODEL_NAME`: LLM model name override (example: `gpt-4o-mini`)
Note: Also installable via uvx (mcp-as-a-judge).
Add this MCP server to my current client's config in the correct format for you. If you need secrets or credentials I haven't provided, ASK me — do not invent values or leave raw placeholders. After adding it, tell me how to verify the server is connected.LLM_API_KEYrequiredRequired for AI assistants without full MCP sampling support.Real-time infrastructure monitoring with metrics, logs, alerts, and ML-based anomaly detection.
Validate oh-my-posh configurations and segment snippets against the official schema.
Real-time Kubernetes network traffic visibility and API analysis for HTTP, gRPC, Redis, Kafka, DNS.