Data catalogue
Find datasets, series, publications and extracts across the marketplace.
- Warehouse rows
- 6.17B
- Structured DB
- 1,711.35 GB
- Live data store
- 189.73 GB
- Data tables
- 1,03,091
- Publications & data products
- 1,749
About these figures
BigQuery reports 6,167,191,751 physical rows across 89 native base tables in 9 catalogue warehouses, measured on . Structured DB measures logical bytes across the same tables. The headline excludes 22 verified staging and backup tables with 365,935,268 rows and 57.49 GB of logical storage. Separate publisher editions can contain overlapping observations.
The earlier 40-MCP inventory reported 1,115,017,091 records across warehouses, files and upstream sources, including 811,051,329 upstream survey records from MoSPI NADA.
Live data store measures 189.73 GB of live catalogue objects across 39 Cloud Storage buckets, measured on .
UNCTAD ingestion files account for 153.97 GB of additional live storage. Retained soft-deleted objects account for 109.84 GB. Raw publisher archives and their corresponding BigQuery tables are measured in their respective storage layers.
Data tables counts 1,03,091 publisher tables held across the marketplace MCPs, measured in each MCP's own store on 2026-11-10: one table, sheet, dataflow, report section or API dataset in one edition counts once, the way data.gov.in counts resources. Raw copies, backups and archive duplicates are not counted twice. They belong to 1,749 publications and data products — six Economic Survey reports are one publication — which hold 10,664 editions, 8,62,701 series and 19,717 documents. India Intelligence and Survey chapter packs are built from these MCPs and are not added. Geo-Spatial can also reach 2,36,742 data.gov.in resources live; they are not held and not counted.
The search index includes 18,933 datasets (tables, survey studies and map layers) and 493 individually indexed series. Separate live and archived access routes can have their own entries. These figures apply across the catalogue, regardless of filters.
All assets
74 results
retrieval status
Per-task status, calls used, values accepted/rejected, and unavailable sources.
run research
Run research briefs as bounded sub-agents (default: all planned briefs, in parallel). Each uses only its allowed sources; every evidence item passes the scope gate.
run retrieval
Run retrieval tasks as bounded agents (default: all planned tasks, in parallel). Every recorded value passes the value gate; rejected values are parked with a reason.
select findings
Keep only findings that serve a Charter sub-question (the rest are parked for the appendix). `keep`/`park` override the decision for specific finding ids afterwards.
set value
Manual override for one cell (value=None clears the override). Logged with the note and shown as 'override' in the workbook.
start dataset job
Start a full build in the background and return at once. Pass `dataset_id` to build a dataset whose spec you already reviewed with define_dataset_spec. Poll get_dataset_job(dataset_id) — it long-polls.
start insight article
Start generate_insight_article as a background job and return at once with its session_id. Poll get_article_job(session_id) every ~20s for stage and progress; the finished article is in its `result`. Same inputs as generate_insight_article.
submit research result
Submit a host sub-agent's evidence for a brief. Each item is scope-gated; rejected items are parked with the reason. final=True (default) also closes the brief; its verdicts/answers merge with any earlier submit (newest wins per hypothesis / sub-question). final=False records the evidence only — use it to submit as you go and read each gate verdict, then close the brief with one final submit (evid
submit values
Host mode: submit values for a task — each {entity, metric, period, value, unit, basis, source_type, source_url|source_ref, source_title, published, locator}, AS PRINTED. Each is gated; rejected ones come back with the reason.
test hypothesis
Test one hypothesis: timing, magnitude, signatures in the user's data, source independence, contradictions → grade and the causal wording it allows.
to insight article
Hand the finished dataset to the Insight Generation MCP to research and write an article answering `question`. Returns its job's session_id (poll it there with get_article_job).
update dataset spec
Edit the Spec: top-level keys in `patch` replace the stored ones (lists replace wholesale), e.g. {"entities": [...]}, {"periods": {"start": "Q1 FY22"}}, {"metrics": [...]}, {"conflict_tolerance_pct": 1}. Re-validated on save.
validate dataset
Completeness per metric, derived-vs-reported mismatches, range checks, fraction-vs- percent slips, scale jumps and outliers against each series' own history.
verify article
Check the draft: every number traces to a fact or evidence, citations exist, causal wording ≤ grade, core question answered up front, every sub-question covered, nothing off-scope. revise=True applies one fix-up pass and re-checks.
Inventory snapshot 15 Nov 2026. Publisher dates and individual source checks can differ.