> For the complete documentation index, see [llms.txt](https://docs.biobox.io/guide/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.biobox.io/guide/how-to/ask-biobox-with-claude/tools/get-delta-report.md). # get\_delta\_report — Get a delta report Get a delta report Get a delta report ## What it does Fetch one delta report (a saved comparison of two-or-more scoring reports of a model) and its side-by-side diff scores — each target's rank and score in every compared report, and how much it moved between them (biggest movers first). **When to use:** the user picked a delta report (from list\_delta\_reports) and wants to see what changed between the compared runs; the user asks which targets moved up/down the most between two scored versions of a model; you need the per-target side-by-side rank/score across the reports a delta report compares. **When NOT to use:** you want the evidence/model diff for ONE target within this delta — how its evidence changed, or why models score it differently (use get\_delta\_report\_target with that target's uuid); you just want the inventory of delta reports, not one report's scores (use list\_delta\_reports); you want the ranked scores of a SINGLE scoring report, not a comparison (use get\_report\_scores); you want the per-criterion evidence behind one target's score in a single report (use get\_evidence). **Parameters:** * `deltaReportId` — the delta report's id (its uuid, from list\_delta\_reports) — which delta report to fetch * `topN` — optional. How many top-mover targets to return in the diff table (default 25, max 100). Targets are ranked by absolute score change between the primary and last report. **Returns:** An object with the delta report's metadata — deltaReportId, displayName, description, type (e.g. "live-vs-archived"), anchorObject ({ uuid, displayName }), models (\[{ modelId, displayName }], modelId → list\_reports), reports (the compared reports \[{ reportId, displayName, archived }] in order — reports\[0] is the PRIMARY, archived flags which entries are archived snapshots vs the live report, reportId → get\_report\_scores), dateCreated, dateUpdated — PLUS the diff: totalTargets (the DENOMINATOR — the full number of movers in the primary report; `targets` returns the top `topN` of these, so if totalTargets > targets.length you are seeing the top slice — raise topN to see more), and targets: the top movers, each { uuid (the target's stable uuid → get\_delta\_report\_target for its evidence/model diff, or get\_entity), displayName, scores (\[{ reportId, rank, score, normScore }] aligned to reports\[] order, nulls if the target is absent from a report), scoreDelta (score(primary) − score(last)), rankDelta (positive = moved up in the primary report) }. NOTE: the diff is anchored on the PRIMARY report (reports\[0]) — totalTargets and targets cover the targets scored in reports\[0]; a target present only in a later compared report is not listed or counted. A bad deltaReportId returns a 404 Not Found error. ## Parameters | Parameter | Type | Required | Description | | --------------- | ------ | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | `deltaReportId` | string | yes | the delta report's id (its uuid, from list\_delta\_reports) — which delta report to fetch | | `topN` | number | no | optional. How many top-mover targets to return in the diff table (default 25, max 100). Targets are ranked by absolute score change between the primary and last report. | ## Response ```ts { deltaReportId: string; displayName: string; description: string | null; type: string | null; anchorObject: { uuid: string | null; displayName: string | null; } | null; models: Array<{ modelId: string; displayName: string | null; }>; reports: Array<{ archived: boolean | null; reportId: string; displayName: string | null; }>; dateCreated: string | null; dateUpdated: string | null; totalTargets: number; targets: Array<{ uuid: string; displayName: string | null; scores: Array<{ reportId: string; rank: number | null; score: number | null; normScore: number | null; }>; scoreDelta: number | null; rankDelta: number | null; }>; } ``` ## Example ```json // get_delta_report({"deltaReportId":"XAQc6AFG_tUe1PfXsABtX"}) { "deltaReportId": "XAQc6AFG_tUe1PfXsABtX" } ``` Response (arrays truncated): ```json { "deltaReportId": "XAQc6AFG_tUe1PfXsABtX", "displayName": "Neurodegenerative Target Prioritization", "description": "Evaluating scientific frameworks for neurodegenerative target prioritization", "type": "live-vs-archived", "anchorObject": { "uuid": "MONDO:0004976", "displayName": "amyotrophic lateral sclerosis" }, "models": [ { "modelId": "d3wyAtDLSewvY90zVcrAP", "displayName": "Neurodegenerative Target Prioritization 3" } ], "reports": [ { "reportId": "pCfdy8OyuPKQmZp", "displayName": "ALS", "archived": true }, { "reportId": "SNV8xUkFBQz1Q4a", "displayName": "ALS", "archived": true }, { "reportId": "FExtbFGkakJWXTXcsOgzT", "displayName": "ALS", "archived": true } ], "dateCreated": "2026-06-05T18:29:36.431Z", "dateUpdated": "2026-06-05T18:29:36.431Z", "totalTargets": 39543, "targets": [ { "uuid": "ENSG00000107290", "displayName": "SETX", "scores": [ { "reportId": "pCfdy8OyuPKQmZp", "rank": 21, "score": 0.827819455787958, "normScore": 0.16895226027115767 }, { "reportId": "SNV8xUkFBQz1Q4a", "rank": 108, "score": 0.4605755222047002, "normScore": 0.12443315603152495 }, { "reportId": "FExtbFGkakJWXTXcsOgzT", "rank": 98, "score": 0.4605755222047002, "normScore": 0.1346151104517618 } ], "scoreDelta": 0.3672439335832578, "rankDelta": 77 }, { "uuid": "ENSG00000110887", "displayName": "DAO", "scores": [ { "reportId": "pCfdy8OyuPKQmZp", "rank": 36, "score": 0.68539146512936, "normScore": 0.13988368646632404 }, { "reportId": "SNV8xUkFBQz1Q4a", "rank": 263, "score": 0.3431301120619701, "normScore": 0.09270306543634646 }, { "reportId": "FExtbFGkakJWXTXcsOgzT", "rank": 253, "score": 0.3431301120619701, "normScore": 0.10028865128012253 } ], "scoreDelta": 0.3422613530673899, "rankDelta": 217 }, { "uuid": "ENSG00000080644", "displayName": "CHRNA3", "scores": [ { "reportId": "pCfdy8OyuPKQmZp", "rank": 33, "score": 0.7046574816425919, "normScore": 0.14381574799685873 }, { "reportId": "SNV8xUkFBQz1Q4a", "rank": 224, "score": 0.36239612857520204, "normScore": 0.09790814283043223 }, { "reportId": "FExtbFGkakJWXTXcsOgzT", "rank": 214, "score": 0.36239612857520204, "normScore": 0.10591964297607619 } ], "scoreDelta": 0.34226135306738986, "rankDelta": 181 }, "… 22 more" ] } ```