"namespace:io.github.warwickwood-cell" matching MCP tools:

• emem_elevation

  emem — Earth memory protocol

  One-shot elevation answer that fuses Cop-DEM 30 m (land), GMRT (ocean topobathy), and ESA WorldCover (water mask) into a single signed scalar at a place or coordinate. Returns `elevation_m`, the source actually used, and a `coherence_note` when the two surfaces disagree at the coast. When to use: Use when the user asks 'how high is X' or 'what's the elevation at this lat/lng' and you want the correct answer regardless of whether the cell is land, water, or coastline — the handler picks Cop-DEM for land and GMRT for water and surfaces the choice. Pass `place` (free text), `lat`+`lng`, OR `cell`. Otherwise, prefer emem_recall with `copdem30m.elevation_mean` / `gmrt.topobathy_mean` individually.

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• emem_jepa_predict

  emem — Earth memory protocol

  Predict next-month NDVI at a cell using a constrained JEPA-pattern AR(2) seasonal predictor. Reads up to 24 past months of `indices.ndvi`, fits a closed-form predictor `y_{t+1} = α·(lag-12 NDVI or recent mean) + β·(last + slope) + γ·recent_mean`, returns the prediction clamped to NDVI's physical range. Coefficients (α=0.6, β=0.3, γ=0.1) are NOT learned — they're fixed from the agricultural-NDVI literature. v2 (future) will train an actual encoder + predictor on the geotessera embedding pool. When to use: Use when the user wants a one-month-ahead NDVI forecast at a specific cell (crop-stress monitoring, growing-season tracking, vegetation-anomaly anticipation). Lookback defaults to 6 months; if fewer monthly tslots are attested at this cell, the predictor uses what's there and surfaces the count in `lookback_months_used`. Returns 422 if no NDVI history exists at the cell — chain to `emem_backfill` first to seed history. Receipt cites every input NDVI fact CID.

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• emem_ndvi

  emem — Earth memory protocol

  Recall Sentinel-2 NDVI (indices.ndvi, 10 m native) at a point or place. Composes locate → cell64 → recall in one call; auto-materializes on miss. When to use: Use when the user names a place (or lat/lng) and just wants the NDVI number. Polygon-resolved places default to a 16-cell fan-out aggregated as mean/median. Set `n_cells: 1` for point behaviour. For multi-band batches use emem_recall.

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• validate_formula

  Dock

  Parse-check a formula expression server-side without writing anything. Returns { ok, error?, rewrittenFormula?, referencedFunctions, unknownFunctions }. Use BEFORE update_row / create_row when the formula references functions or syntax you're not 100% sure of: a `=SUMIFS(...)` with the wrong arg order or a misspelled `=AVERAG(...)` will round-trip into the cell as a stored carrier with no value, and the user will see #NAME? or #VALUE? on next view. Catch it here. `unknownFunctions` flags any identifier that isn't in the Dock Sheets catalog (including likely typos); `referencedFunctions` lists the canonical post-alias names the engine will see. Cheap, public, no auth, no workspace context needed.

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• emem_hunt

  emem — Earth memory protocol

  Event-discovery sweep: pick an event keyword (algal_bloom, deforestation, flood_extent, wildfire, urban_heat_island, methane_plume, landslide, drought, soil_salinity, crop_stress, water_turbidity, oil_slick) plus a region (free-text name or polygon_bbox). The responder geocodes the region, fans out across up to 32 sampled cells, recalls each event's primary scalar input band, and returns the top 8 hotspots ranked by that scalar — each carrying its cell64, lat/lng, the recalled value, a fact_cid for citation, and a scene.png URL. Bypass for free-text input is `emem_ask` (the classifier in /v1/ask routes "find X in Y" questions to the same hunter path). When to use: Call when the user asks an open-world discovery question ("find oil spills in the Persian Gulf", "where is deforestation happening in the Amazon", "show me algal blooms in Lake Erie", "hunt wildfires across California"). Surface 3–8 hotspots with their scene.png as image attachments and quote at least one fact_cid. For `oil_slick` the responder honestly reports `not_yet_implemented` and points at SAR-darkening + turbidity proxies — don't fabricate detections. The ranking uses the algorithm's primary scalar input only; for the full per-cell algorithm score, fetch the formula at /v1/algorithms/<key> and apply it client-side over the same recalled bands.

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• list_sheet_functions

  Dock

  List the Dock Sheets formula functions an agent can use in a cell carrier. Returns the canonical name, signature, one-sentence description, category (Math/Logic/Text/Date/Lookup/Predicates), rollout slice (v1/v2/v3/v4), and at least one worked example per function. Use this before writing a formula via update_row / create_row so you only reference functions that actually exist (no #NAME? errors). Also returns the alias map (e.g. CONCAT → CONCATENATE) so you can pick the canonical name even when writing the alias the UI accepts. Optional filters: `category` narrows to one category, `slice` narrows to one rollout slice, `name` substring-matches names + descriptions + signatures. Public, no auth, no rate limit beyond global.

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• emem_field_boundaries

  emem — Earth memory protocol

  Per-field agricultural-boundary polygons from the Fields of The World global product (~3.17B fields, 241 countries, 10 m resolution, CC-BY-4.0). Returns a GeoJSON FeatureCollection with the polygon geometries, FIBOA-compatible properties, and a planar `area_m2` per field — plus provenance (source CID, provider URL, license, attribution). When to use: Call when the user asks about farms, fields, parcels, croplands, plots, or agricultural boundaries inside a region — anywhere the OSM/Nominatim boundary alone is too coarse (the OSM polygon for a farm is its estate envelope; this returns the individual field polygons inside). Pass `place` (free-text) or `polygon_bbox`. For farms wider than ~10 km², split the bbox: the fetcher caps each call at 16 covering tiles. The receipt quotes `license: CC-BY-4.0` and `attribution: Fields of The World / Taylor Geospatial Institute` — surface both with any rendered map. For a one-shot "facts at every cell inside the farm PLUS the field polygons", call `emem_recall_polygon` with `include: ["ftw_fields"]` instead.

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• emem_cell_scene_rgb

  emem — Earth memory protocol

  True-colour Sentinel-2 L2A RGB thumbnail centred on a cell. PNG returned as a native MCP ImageContent block (mimeType image/png). Pure-Rust pipeline: STAC search + HTTP-Range COG reads + 2-98 percentile stretch + PNG encode. When to use: Call when the user wants a VISUAL of a place — 'show me what this looks like', 'before/after the flood', 'is there a forest here', 'is this developed'. Returns a 256×256 px RGB image (~2.56 km × ~2.56 km at S2's 10 m native resolution), centred on the cell. Pass `cell` as a cell64 string OR a place name (auto-resolved). `max_cloud` filters scenes by `eo:cloud_cover` (default 20 %); raise it (60–80 %) for cloud-prone tropics if you keep getting 'no scene' errors. `datetime` is an RFC 3339 interval like `"2024-01-01T00:00:00Z/2024-12-31T00:00:00Z"` for a temporal slice (defaults to last 90 days). `structuredContent` carries the STAC item id, capture time, cloud_cover, EPSG, and per-channel reflectance percentile stretch values used — quote those alongside the image so the receipt is reproducible.

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• emem_state_diff

  emem — Earth memory protocol

  Vector delta between the same cell at two tslots: returns the per-element residual, its L2 norm (scalar change-magnitude), the cosine between the two source vectors (orientation drift), and both source fact CIDs so the agent can quote both attestations as evidence. When to use: Call when the user asks 'how much did X change between A and B' for a foundation embedding at one place. Pass `tslot_a` and `tslot_b` (must differ); default `encoder=geotessera`. For per-band scalar change (NDVI delta, elevation delta) use `emem_diff` instead.

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• asterwise_get_lo_shu_grid

  Asterwise

  Derives a Lo Shu three-by-three frequency grid from birth-date digits and annotates planes, missing or repeated digits, and per-digit traits. SECTION: WHAT THIS TOOL COVERS Chinese Lo Shu analysis: counts how often each digit one through nine appears in the date string, lays counts into the classical magic-square positions, and adds plane_analysis plus number_analysis entries keyed by digit strings '1'..'9'. Zero digits are ignored for placement. It does not compute Pythagorean Life Path (asterwise_get_numerology_profile) or Chaldean compounds. SECTION: WORKFLOW BEFORE: None — standalone. AFTER: None. SECTION: INPUT CONTRACT date string only; validated upstream. SECTION: OUTPUT CONTRACT data.birth_date (string) data.grid — three-by-three nested int array (row-major): row positions map to numbers [4,9,2], [3,5,7], [8,1,6] respectively; cell value = count of that digit in the date (0 if absent) data.present_numbers[] (int array) data.missing_numbers[] (int array) data.repeated_numbers[] (int array — digits appearing at least twice) data.plane_analysis: thought_plane — { numbers[] (int array), description (string), complete (bool) } will_plane — same shape action_plane — same shape golden_yod — same shape silver_yod — same shape data.number_analysis{} — keys '1' through '9' (string keys): count (int) plane (string) trait (string) status (string — 'missing', 'present', or 'strong') note (string) SECTION: RESPONSE FORMAT response_format=json serialises the complete response as indented JSON — use this for programmatic parsing, typed clients, and downstream tool chaining. response_format=markdown renders the same data as a human-readable report. Both modes return identical underlying data — no fields are added, removed, or filtered by either mode. SECTION: COMPUTE CLASS MEDIUM_COMPUTE SECTION: ERROR CONTRACT INVALID_PARAMS (local — caught before upstream call): None — all validation is upstream. INVALID_PARAMS (upstream): — None — upstream rejection surfaces as MCP INTERNAL_ERROR at the tool layer. INTERNAL_ERROR: — Any upstream API failure or timeout → MCP INTERNAL_ERROR Edge cases: — Zeros in ISO dates are skipped — only digits one through nine populate the grid. SECTION: DO NOT CONFUSE WITH asterwise_get_numerology_profile — letter-based Western numbers, not digit-frequency Lo Shu. asterwise_get_name_correction — spelling harmonics, not birth-date grids.

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• emem_cell_geojson

  emem — Earth memory protocol

  Cell polygon as a native MCP EmbeddedResource (mimeType application/geo+json). Properties carry centre lat/lng, bbox, approx size in metres, and the 8-cell neighbourhood — drop straight into Mapbox / Leaflet / Deck.gl / QGIS without a GIS pipeline. When to use: Call when the agent (or a downstream renderer) needs the cell as geographic geometry — for map overlays, polygon-clipping ops, or feeding a styling pipeline. Pass `cell` as cell64 or place name. The result is a GeoJSON Feature with Polygon geometry; for a FeatureCollection that includes every recalled fact's value as a property, fetch /v1/cells/{cell64}/recall_geojson?bands=... over plain REST instead.

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• biobtree_map

  BioBTree

  Map identifiers between databases. SYNTAX: biobtree_map(terms="ID", chain=">>source>>target") - Chain MUST start with ">>" - Source MUST match input ID type ID TYPE → SOURCE: - ENSG* → >>ensembl - P*/Q*/O* → >>uniprot - CHEMBL* → >>chembl_molecule - GO:* → >>go - MONDO:* → >>mondo - HP:* → >>hpo - HGNC:* or gene symbols → >>hgnc SOME DRUG EXPLORATION PATHS: - >>chembl_molecule>>chembl_target>>uniprot (drug targets) - >>pubchem>>pubchem_activity>>uniprot (bioactivity) - >>gtopdb_ligand>>gtopdb_interaction>>gtopdb>>uniprot (curated pharmacology with affinity data) - >>ensembl>>reactome>>chebi (pathway chemicals - when no direct targets) - Discover more via entry xrefs + EDGES WARNING - GO terms with high xref_count (>100): - Don't map GO → proteins → drugs (too many results) - Instead: search drug class for condition → verify targets this GO term DISEASE GENE PATTERNS: - >>mondo>>gencc>>hgnc (curated) - >>mondo>>clinvar>>hgnc (variant-based) DISEASE → DRUG PATTERNS: - >>mesh>>chembl_molecule (MeSH disease/condition → drugs with indications) - >>mondo>>clinical_trials>>chembl_molecule (disease → trial drugs) DISCOVERY APPROACH: - Use biobtree_entry to see xrefs (what's connected) - Use EDGES above to see where each dataset leads - Build chains based on what connections exist for YOUR entity RETURNS: mapped identifiers with dataset and name EDGES (what connects to what): ensembl: uniprot, go, transcript, exon, ortholog, paralog, hgnc, entrez, refseq, bgee, gwas, gencc, antibody, scxa hgnc: ensembl, uniprot, entrez, gencc, pharmgkb_gene, msigdb, clinvar, mim, refseq, alphafold, collectri, gwas, dbsnp, hpo, cellphonedb entrez: ensembl, uniprot, refseq, go, biogrid, pubchem_activity, ctd_gene_interaction refseq: ensembl, entrez, taxonomy, ccds, uniprot, mirdb mirdb: refseq transcript: ensembl, exon, ufeature, alphamissense uniprot: ensembl, alphafold, interpro, pfam, pdb, ufeature, intact, string, string_interaction, biogrid, biogrid_interaction, chembl_target, go, reactome, rhea, swisslipids, bindingdb, antibody, pubchem_activity, cellphonedb, jaspar, signor, diamond_similarity, esm2_similarity, alphamissense alphafold: uniprot interpro: uniprot, go, interproparent, interprochild chembl_molecule: mesh, chembl_activity, chembl_target, pubchem, chebi, clinical_trials chembl_activity: chembl_molecule, chembl_assay, bao chembl_assay: chembl_activity, chembl_target, chembl_document, bao chembl_target: chembl_assay, uniprot, chembl_molecule pubchem: chembl_molecule, chebi, hmdb, pubchem_activity, pubmed, patent_compound, bindingdb, ctd, pharmgkb pubchem_activity: pubchem, ensembl, uniprot chebi: pubchem, rhea, intact swisslipids: uniprot, go, chebi, uberon, cl lipidmaps: chebi, pubchem dbsnp: hgnc, clinvar, pharmgkb_variant, alphamissense, spliceai clinvar: hgnc, mondo, hpo, dbsnp, orphanet alphamissense: uniprot, transcript gwas: gwas_study, efo, dbsnp, hgnc, mondo gwas_study: gwas, efo, mondo mondo: gencc, clinvar, efo, mesh, hpo, clinical_trials, antibody, cellxgene, cellxgene_celltype, orphanet, mondoparent, mondochild, gwas, gwas_study gencc: mondo, hpo, hgnc, ensembl clinical_trials: mondo, chembl_molecule pharmgkb: hgnc, dbsnp, mesh, pharmgkb_gene, pharmgkb_variant, pharmgkb_clinical, pharmgkb_guideline, pharmgkb_pathway pharmgkb_variant: pharmgkb_clinical, hgnc, mesh, dbsnp pharmgkb_gene: hgnc, entrez, ensembl, pharmgkb pharmgkb_clinical: dbsnp, hgnc, mesh, pharmgkb_variant pharmgkb_guideline: hgnc, pharmgkb pharmgkb_pathway: hgnc, pharmgkb ctd: mesh, ctd_gene_interaction, ctd_disease_association, pubchem ctd_gene_interaction: ctd, entrez, taxonomy, pubmed ctd_disease_association: ctd, mesh, mim, pubmed intact: uniprot, chebi, rnacentral string: uniprot, string_interaction string_interaction: string, uniprot biogrid: entrez, uniprot, refseq, taxonomy bgee: ensembl, uberon, cl, taxonomy, bgee_evidence bgee_evidence: bgee, uberon, cl cellxgene: cl, uberon, mondo, efo, taxonomy cellxgene_celltype: cl, uberon, mondo scxa: cl, uberon, taxonomy, ensembl, scxa_gene_experiment scxa_expression: ensembl, scxa, scxa_gene_experiment scxa_gene_experiment: ensembl, scxa, scxa_expression, cl rnacentral: uniprot, ensembl, intact, hgnc, refseq, ena reactome: ensembl, uniprot, chebi, go, reactomeparent, reactomechild rhea: chebi, uniprot, go go: ensembl, uniprot, reactome, msigdb, swisslipids, bgee, interpro, goparent, gochild hpo: clinvar, gencc, mondo, msigdb, orphanet, mim, hmdb, hgnc, hpoparent, hpochild efo: gwas, mondo, cellxgene, efoparent, efochild uberon: bgee, cellxgene, cellxgene_celltype, swisslipids, uberonparent, uberonchild cl: bgee, cellxgene, cellxgene_celltype, scxa, scxa_gene_experiment, clparent, clchild taxonomy: ensembl, uniprot, bgee, biogrid, ctd_gene_interaction, taxparent, taxchild mesh: pharmgkb, ctd, ctd_disease_association, pubchem, mondo, chembl_molecule, meshparent, meshchild eco: ecoparent, ecochild antibody: ensembl, uniprot, mondo, pdb msigdb: hgnc, entrez, go, hpo orphanet: hpo, uniprot, mondo, hgnc, clinvar, mim, mesh mim: clinvar, hpo, mondo, uniprot, ctd_disease_association hmdb: pubchem, hpo, chebi, uniprot collectri: hgnc # transcription factor → target gene interactions esm2_similarity: uniprot # protein structural similarity diamond_similarity: uniprot # protein sequence similarity cellphonedb: uniprot, ensembl, hgnc, pubmed # ligand-receptor pairs for cell-cell communication spliceai: hgnc pdb: uniprot, go, interpro, pfam, taxonomy, pubmed fantom5_promoter: ensembl, hgnc, entrez, uniprot, uberon, cl fantom5_enhancer: ensembl, uberon, cl fantom5_gene: ensembl, hgnc, entrez jaspar: uniprot, pubmed, taxonomy encode_ccre: taxonomy bao: chembl_activity, chembl_assay, baoparent, baochild brenda: uniprot, pubmed, brenda_kinetics, brenda_inhibitor brenda_kinetics: brenda brenda_inhibitor: brenda gtopdb: uniprot, hgnc, gtopdb_ligand, gtopdb_interaction # drug targets (GPCRs, ion channels, enzymes) gtopdb_ligand: pubchem, chebi, chembl_molecule, gtopdb_interaction # ligands/drugs with binding data gtopdb_interaction: gtopdb, gtopdb_ligand, pubmed # target-ligand binding with affinity values FILTER SYNTAX: >>dataset[field operator value] OPERATORS: == equals >>dataset[field=="value"] != not equals >>dataset[field!="value"] > greater than >>dataset[field>value] < less than >>dataset[field<value] >= greater or equal >>dataset[field>=value] <= less or equal >>dataset[field<=value] contains string match >>dataset[field.contains("value")] LOGICAL OPERATORS: && AND >>dataset[field1>5 && field2<10] || OR >>dataset[field=="A" || field=="B"] ! NOT >>dataset[!field] or >>dataset[!(field=="value")] TYPE RULES: - FLOAT: use decimal point (70.0 not 70) - INT: no decimal (2 not 2.0) - STRING: quote values ("Pathogenic", "PHASE3") - BOOL: true/false (no quotes) EXAMPLES: >>chembl_molecule[highestDevelopmentPhase==4] # approved drugs >>chembl_molecule[highestDevelopmentPhase>=3] # Phase 3+ >>clinical_trials[phase=="PHASE3"] >>go[type=="biological_process"] >>clinvar[germline_classification=="Pathogenic"] >>reactome[name.contains("signaling")] >>gtopdb[type=="gpcr"] # GPCR targets >>gtopdb[type=="ion_channel"] # ion channel targets >>gtopdb_ligand[approved==true] # approved drugs only >>gtopdb_interaction[endogenous==true] # endogenous ligand interactions

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• emem_trajectory

  emem — Earth memory protocol

  Time series for one (cell, band) over an inclusive [start, end] tslot window. Returns only what's already attested — does NOT trigger materialization. For historical backfill use `emem_backfill`. When to use: Call when the user asks 'how did X change over time' for a band that already has multiple historical tslots seeded. IMPORTANT differences from `emem_recall`: (1) trajectory does NOT auto-materialize past tslots — it returns only facts that have already been attested at this responder, so for fast-tempo bands like `indices.ndwi` you'll typically see ONE point at the latest tslot until an attester seeds history. (2) tslots are non-negative `u64`; there's no negative-offset 'last 2 years' shorthand. For LONG-TERM history questions ('flooded in last 2 years', 'forest loss since 2020') prefer either (a) a static-tempo summary band that one fact answers — `surface_water.recurrence` covers 1984-2021 in a single signed value, no trajectory needed — or (b) `emem_backfill` to materialize and sign the missing tslots in one call.

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• set_cell_contents

  Bilig WorkPaper

  Write raw content to one cell and recalculate dependents in memory only. Start with --writable when the edit should persist to JSON.

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• evaluate_formula

  Dock

  Evaluate a formula expression against an actual Dock workspace's columns + rows, server-side, returning the same display value the UI's HyperFormula engine would render. Two modes: STANDALONE (omit `workspace_slug`) — evaluates against an empty grid; useful for `=SUM(1, 2, 3)` or any formula with no cell references. IN-WORKSPACE (pass `workspace_slug`, optionally `at`) — loads the workspace's grid, evaluates the formula as if pasted into the `at` cell (or A1 if omitted), resolves real refs against actual data. Returns { ok, displayValue, error? }. Workspace mode requires read access; standalone mode is public.

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• emem_grid_info

  emem — Earth memory protocol

  Active grid encoding: cell64 ground resolution, lat/lng axis sizes, DGGS lineage. When to use: Call once at session start (or when the user asks about cell resolution / 'how big is a cell'). Returns the actual ground resolution today (~9.54 m × 9.55 m square at the equator (lat 21 bits × lng 22 bits, matching Sentinel-1/Sentinel-2 native pixel pitch). The cell64 bit layout reserves a resolution-tag field for future hierarchical refinement targeting H3-equivalent res-13 (~3.4 m) cells in v0.1.) and the spec target. Useful before you reason about whether one cell is enough or whether you need `emem_recall_polygon`.

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• emem_state_multi

  emem — Earth memory protocol

  Fans out across every wired foundation-embedding encoder (`geotessera`, `clay_v1`, `prithvi_eo2`) for one cell and returns a structured per-encoder state map. Each encoder is attempted independently; encoders that fail at this cell surface under `missing` with a typed reason instead of killing the request. When to use: Call when the agent wants cross-encoder consensus (do Tessera, Clay, and Prithvi agree on the archetype here?), redundancy-aware reasoning (which encoder is freshest at this cell?), or a concatenated multi-encoder state for downstream linear probes. Pass `encoders: [...]` to override the default foundation set.

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• emem_jepa_predict_v2

  emem — Earth memory protocol

  Predict the next-vintage 128-D Tessera embedding at a cell using a small learned dynamics MLP. Reads the K=3 most-recent attested `geotessera.YYYY` vintages, runs them through an ONNX dynamics head (~200k params, CPU-fast), returns the predicted next-year embedding. The receipt's `model` block carries `model_id`, `version`, `blake2b_hex` (model_cid), training/validation provenance, and `honesty_warnings` flagging `untrained_baseline` when the artifact is the zero-init sentinel. Distinct from v1 (`emem_jepa_predict`) — v1 returns an NDVI scalar via closed-form coefficients; v2 returns a 128-D embedding from a learned model. When to use: Use when you want a forecast in EMBEDDING space rather than NDVI scalar — e.g. to find next-year analogs via `emem_find_similar` against the prediction, or to feed any algorithm in `algorithms_for_topic.foundation_embedding`. Returns 422 with a `/v1/backfill` hint when the cell has fewer than 3 consecutive Tessera vintages cached. Always read the receipt's `model.honesty_warnings` array — when it contains `untrained_baseline`, the prediction is the trivial 'predict last vintage' baseline (treat as no-op).

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• emem_state

  emem — Earth memory protocol

  Dense state vector for a place. Two views: `encoder` (single foundation embedding at its native dim — 128-D Tessera, 1024-D Clay, 1024-D Prithvi) and `cube` (the full 1792-D voxel concatenated across every wired band, with per-band coverage manifest and full-fidelity extras for any encoder whose native dim exceeds its cube slot). When to use: Call when the agent needs a dense, ready-to-feed vector for downstream similarity / linear-probe / clustering, or wants a single rebindable handle (`memory_token` / `state_cid`) for a place. Default `view=encoder` (cheap, single recall) — pass `encoder` (default `geotessera`) to pick the band. Pass `view=cube` for the responder's full attested view at the cell; the response carries `coverage[]` so an agent can distinguish attested-zero (signed Absence) from not-yet-materialised, and `extras[]` preserves full Clay/Prithvi 1024-D vectors when the cube truncates to a 384-D slot. Pair with `emem_find_similar` for k-NN, `emem_compare` for two-cell cosine, or `emem_verify_receipt` to verify the signed payload offline.

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• emem_eudr_dds

  emem — Earth memory protocol

  Produce a Due Diligence Statement per Regulation (EU) 2023/1115 for one or more plots. Each plot carries operator-supplied geometry (GeoJSON Polygon for >4 ha, Point for ≤4 ha non-cattle per Article 2(28)), country of production (ISO3), Combined Nomenclature code (HS-6+), and quantity in kg. The endpoint applies the regulation's 10 % canopy / 0.5 ha / 5 m height forest definition (Article 2(4)) using the EU Commission's expected JRC GFC2020 V3 baseline plus Hansen GFC v1.12 loss-year confirmation; Sims et al. 2025 driver attribution and RADD SAR fallback layer on when those connectors are wired (Absence today). The response is an Annex II-shaped envelope with per-plot verdict (pass/fail/not_in_scope/indeterminate/fail_below_de_minimis), failing-cell fraction, and signed fact CIDs for every per-cell verdict — operators quote them in the company's Article 12 record. Article 9(1)(b) legality (land tenure, FPIC, country-of-origin laws) is structurally out of EO scope; the response carries an explicit `legality_disclaimer` for that reason. When to use: Call when a commodity supplier or EU importer needs to evidence due diligence under Regulation (EU) 2023/1115. Use the plot-level signed receipts as evidence inside the operator's company record; pair with a partner legality module before submitting the final DDS to the EU Information System (TRACES NT). For a single plot, pass one entry in `plots`. For batch supply-chain audits, pass up to a few dozen plots in one call — the endpoint fans out per plot. Surface the failing-cell fraction, the chosen forest baseline, and the legality disclaimer in the user-facing response so the operator understands what the engine claims (and does not).

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