Interpret RAG drift scores, get threshold recommendations, and understand drift dimensions via @mukundakatta/ragdrift-mcp (npx)

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## @mukundakatta/ragdrift-mcp v latest — RAG drift score interpreter & threshold recommender

**Install & run:** `npm install --prefix /tmp/ragdrift-mcp @mukundakatta/ragdrift-mcp`, entry point `src/index.js`.

### Tools (3)

| Tool | Params | Returns |
|------|--------|---------|
| `interpret_drift_score` | `{score: number, dimension: enum, threshold?: number}` | `{dimension, score, threshold, exceeded, severity, method_used, interpretation, next_steps}` |
| `recommend_thresholds` | `{dimension: enum, sample_size?: int≥50, false_positive_budget?: 0.005-0.5}` | `{dimension, sample_size, false_positive_budget, recommended: {conservative, moderate, lax}, rationale}` |
| `explain_drift_dimensions` | `{}` | `{dimensions: [{name, catches, methods[], typical_score_range, suggested_thresholds, notes}]}` |

**Dimensions:** `data` | `embedding` | `response` | `confidence` | `query`

### Key findings from 12 verified calls

1. **Severity classification is heuristic, not from real data.** The server does NOT compute drift from actual distributions — it interprets a SCORE you already have. You provide the number; it tells you what it means. This is a lookup/advisory tool, not a detector.

2. **Four severity levels:** "no significant shift" (≈0), "moderate shift, watch closely" (low), "significant shift, investigate" (medium), "severe shift, action required" (high). Breakpoints vary by dimension.

3. **Threshold comparison via `exceeded` field:** pass `threshold` to get `exceeded: true/false`. Score 0.35 with threshold 0.3 → `exceeded: true`. Score 0.15 with threshold 0.2 → `exceeded: false`.

4. **`recommend_thresholds` scales by sample size** using `sqrt(1000/n)` — larger samples get tighter thresholds. n=5000 with fp_budget=0.01 for embedding → conservative=0.1875, moderate=0.375, lax=0.75. n=100 with fp_budget=0.2 for response → conservative=0.21, moderate=0.42, lax=0.84.

5. **Statistical methods referenced per dimension:**
   - data: KS + PSI (credit-risk industry bands)
   - embedding: MMD² RBF + Sliced Wasserstein-1
   - response: KS on lengths + optional SW on embeddings
   - confidence: KS + |ECE difference|
   - query: k-means + symmetric KL divergence

6. **Zero score returns "no significant shift"** with "Nothing to do. Continue monitoring." — clean baseline case.

7. **Sub-millisecond latency** — all 12 calls at p50=0ms after first call (1ms JIT).

### Gotchas

- **NOT a drift detector** — it doesn't analyze your data. It's a SCORE INTERPRETER. You still need a separate system to compute the drift score (using KS, PSI, MMD², etc.).
- **Next steps are generic but contextually correct** — e.g. embedding dimension suggests "Did the embedding model change? Was the corpus re-indexed?" which is the right question.
- **`threshold` param is optional** — omit it and `exceeded` is `null` (no comparison).
- **`sample_size` minimum is 50** — schema enforces this.
- **Thresholds are NOT empirically derived** — they're heuristic defaults scaled by a formula, not fit to real drift distributions.

Tool	Params	Returns
`interpret_drift_score`	`{score: number, dimension: enum, threshold?: number}`	`{dimension, score, threshold, exceeded, severity, method_used, interpretation, next_steps}`
`recommend_thresholds`	`{dimension: enum, sample_size?: int≥50, false_positive_budget?: 0.005-0.5}`	`{dimension, sample_size, false_positive_budget, recommended: {conservative, moderate, lax}, rationale}`
`explain_drift_dimensions`	`{}`	`{dimensions: [{name, catches, methods[], typical_score_range, suggested_thresholds, notes}]}`

Interpret RAG drift scores, get threshold recommendations, and understand drift dimensions via @mukundakatta/ragdrift-mcp (npx)

@mukundakatta/ragdrift-mcp v latest — RAG drift score interpreter & threshold recommender

Tools (3)

Key findings from 12 verified calls

Gotchas

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