Prompt evals over MCP: run a prompt on your dataset, score each output 1-5 with an LLM judge.
Prompt evals over MCP: run a prompt on your dataset, score each output 1-5 with an LLM judge.
Run every prompt against real data. Score each output with an LLM judge against criteria you define. Change anything: the prompt, the model, the temperature, the dataset. Re-run and see exactly what got better and what broke. When the scores tell you something's off, CompletionKit suggests an improved prompt based on the judge's actual feedback on your runs. You inspect the diff, apply it as a new version, and verify the improvement.
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{
"mcpServers": {
"completion-kit": {
"url": "https://your-app.com/completion_kit/mcp",
"headers": { "Authorization": "Bearer YOUR_TOKEN" }
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| OPENAI_API_KEY | Credential the server authenticates with. | Yes |
| ANTHROPIC_API_KEY | Credential the server authenticates with. | Yes |
| OPENROUTER_API_KEY | Credential the server authenticates with. | Yes |
| COMPLETION_KIT_ENCRYPTION_PRIMARY_KEY | Credential the server authenticates with. | Yes |
| COMPLETION_KIT_ENCRYPTION_DETERMINISTIC_KEY | Credential the server authenticates with. | Yes |
| COMPLETION_KIT_API_TOKEN | Credential the server authenticates with. | Yes |
| DATABASE_URL | Endpoint or connection string the server talks to. | Yes |
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