emem

Verifiable memory substrate for AI agents. Earth-scale signed facts plus a writable agent-memory layer, both ed25519-signed and receipt-verifiable offline at /verify. Bi-temporal recall (as_of_tslot for valid time, as_of_signed_at for transaction time), CoALA-typed memory files, capability-bound writes, signed bundles (memb:<bundle_cid>), multi-attester contradiction scoring. No API keys.

Skills

• Resolve place to cell64 + band inventory
  Resolves a place mention (free-text name, address, or lat/lng) to the protocol's cell64 identifier, and returns the topic-grouped inventory of bands and algorithms available at that location.
  readL0
• Ask a free-text question about a place
  Single-shot free-text answer about a real-world location, backed by signed satellite/elevation/water/built-up receipts. Forwards a place mention plus a question; runs the locate → recall → algorithm chain server-side; returns one packaged envelope.
  readL0
• Hunter mode — find event hotspots over a region
  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).
  readL0
• EUDR Due Diligence Statement — polygon-in, signed Annex II envelope out
  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/below_mmu), 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.
  readL0
• Standardized Precipitation Index (McKee 1993) drought metric
  Compute the Standardized Precipitation Index (McKee et al. 1993) at a cell: fit a gamma distribution to the same-window precipitation-accumulation history, then standardize the current accumulation to a z-score and map it to a drought class (extreme/severe/moderate drought … normal … wet). Supply `precip_history_mm` + `current_accumulation_mm` directly, or omit them to read the stored `weather.precipitation_mm` trajectory and build the window accumulations server-side. `window_days` selects SPI-1 (30 d), SPI-3 (90 d, default), SPI-12 (360 d), etc.
  readL0
• Burn severity (dNBR, Key & Benson) from pre/post-fire NBR
  Compute the differenced Normalized Burn Ratio (dNBR = NBR_pre − NBR_post; Key & Benson 2006) and map it to the USGS burn-severity classes (unburned / low / moderate-low / moderate-high / high). Supply `nbr_pre` + `nbr_post` (pin the scenes bracketing the fire date) for a correct result, or omit both to use the two most-recent stored `indices.nbr` scenes (older=pre, newer=post) as a coarse estimate.
  readL0
• Rice-paddy methane (IPCC 2019 Tier 2, Eq 5.1)
  Estimate seasonal CH4 emissions from rice cultivation per IPCC 2019 Refinement Eq 5.1: integrate the daily emission factor over the cultivation period with water-regime scaling (SFp pre-season, SFo organic amendment) and an optional Yan-2005 Q10 temperature modifier. `cultivation_period_days` and the regional `efc_kg_ch4_ha_day` (Table 5.11) are REQUIRED — the endpoint refuses to guess a global default because the regional EFc drives the magnitude (~30% bias if wrong). An NDWI series (supplied or read from stored `indices.ndwi`) informs the flooding-regime context.
  readL0
• Deforestation alert proxy (NDVI drop + embedding change)
  Composite deforestation-alert score: `alert_score = 0.5·clamp01(ndvi_drop/0.30) + 0.5·clamp01(embedding_change/0.20)`, where `ndvi_drop = max(0, ndvi_modis_baseline − ndvi_now)` and `embedding_change = 1 − cos(tessera_latest, tessera_prev)`. Each half degrades INDEPENDENTLY and honestly: if a band is missing, that half is dropped AND the output is renamed so a half-score can never be mistaken for the full composite. If NEITHER half is computable the response is a signed `inconclusive` carrying no number.
  readL0
• Sentinel-1 SAR forest-disturbance scout (cloud-penetrating)
  Cloud- and night-independent Sentinel-1 C-band confirmation of forest disturbance. Intact forest scatters VV strongly + stably (canopy volume scattering); clearing collapses that term so VV backscatter DROPS ~3-5 dB. Samples VV at a baseline-year July-1 anchor and the latest scene, reports `vv_drop_db = baseline − recent` and a `disturbed` flag when the drop ≥ 3 dB (Reiche et al. 2018, RSE 204:147). Both VV reads are signed Primary facts; the response cites both fact_cids. Honest `inconclusive` when either S1 vintage is unavailable. Source: Microsoft Planetary Computer sentinel-1-rtc (anonymous SAS — no requester-pays, no API key).
  readL0
• Clay+Prithvi+Tessera change-consensus ensemble
  Three-encoder change ensemble: compute the cosine change between the two most-recent DISTINCT vintages for each of the Clay, Prithvi, and Tessera embeddings at the cell, then vote each encoder's change against `consensus_threshold` (registry default 0.15). Returns each encoder's change magnitude, its vote, and the consensus verdict (how many of the three agree change happened). Degrades to a signed `inconclusive` when the GPU sidecar is unreachable or a cell lacks two distinct vintages for the encoders.
  readL0
• Terrain triad — slope + ruggedness + topographic position from DEM
  Compute three standard DEM terrain indices from one 3×3 Copernicus-DEM (copdem30m.elevation_mean) neighbourhood at a cell: Horn (1981) slope in degrees, Riley (1999) Terrain Ruggedness Index (TRI = sqrt(Σ(Z_centre−Z_i)²)), and Weiss (2001) Topographic Position Index (TPI = Z_centre − mean(neighbours); positive = ridge, negative = valley). The 8 neighbour cell64s are derived by perturbing the cell's lat/lng one cell pitch per axis; the east-west ground spacing is cos(lat)-corrected.
  readL0
• Region similarity — cosine of two regions' mean GeoTessera embeddings
  Answer 'how alike are these two places?' Mean-pool the 128-D GeoTessera embedding across each region's cells to get a centroid, then return the cosine similarity in [-1,1] (+1 = identical landscape, 0 = unrelated). Each region is {place} | {polygon_bbox} | {cells}. CPU-fetched embeddings — no GPU sidecar needed. Surfaces how many cells in each region actually carried a vector (coverage).
  readL0
• Embedding centroid — mean-pooled GeoTessera vector for a region
  Mean-pool the 128-D GeoTessera embedding over a region's cells: centroid = (1/N) Σ v_i, plus the L2-normalised centroid and a content-addressed centroid_cid. The building block region_similarity composes. Region is {place} | {polygon_bbox} | {cells}. NaN dims are averaged over their finite contributors. CPU-only.
  readL0
• Embedding diversity — landscape heterogeneity over a region
  Quantify how varied a region's landscape is: diversity = (1/(N(N-1))) Σ_{i<j} (1 − cosine(v_i, v_j)), the mean pairwise cosine distance over the region's GeoTessera embeddings. 0 = perfectly uniform; higher = more heterogeneous land cover (a determinantal-point-process / k-medoid diversity). Region is {place} | {polygon_bbox} | {cells}. CPU-only.
  readL0
• Neighbourhood consistency / spatial outlier (GeoTessera vs 8 neighbours)
  Score how much a cell looks like its surroundings: consistency = (1/8) Σ cosine(centre, neighbour_i) over the 8 immediate cell64 neighbours, plus outlier_score = 1 − consistency. High consistency = the cell blends in (Tobler's First Law); high outlier_score = it stands out — an edge, a fresh clearing, a built patch in farmland. CPU-only GeoTessera embeddings.
  readL0
• Read the place's state vector (single encoder OR full 1792-D cube)
  Get one dense numeric fingerprint that summarises everything known about a place — ready to feed into similarity search, a classifier, or clustering. Two views: `encoder` returns a single AI-model embedding (128-D Tessera, 1024-D Clay, 1024-D Prithvi); `cube` returns the full 1792-D vector concatenated across every band, with a per-band coverage manifest.
  readL0
• Multi-encoder state at one cell (foundation fan-out)
  Get the place's fingerprint from several AI models at once (`geotessera`, `clay_v1`, `prithvi_eo2`, `galileo`) in one call, returned as a per-model map. Each model is tried independently; any that can't produce a vector here show up under `missing` with a reason instead of failing the whole request.
  readL0
• Between-tslot state vector delta (residual + cosine)
  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.
  readL0
• Compose a memory_token citation handle
  Compose a `memt:<cell64>:<fact_cid>` (or `memt:<cell64>:<state_cid>`) citation handle. Validates both components are non-empty and do not contain the outer separator `:`.
  readL0
• Dereference a memory_token in one round-trip
  Parse a `memt:<cell64>:<fact_cid>` citation handle and return the signed fact body the cid binds. Saves the agent from string-splitting the token and chaining `GET /v1/facts/<cid>` manually.
  readL0
• Compose a signed multi-fact memory bundle
  Compose N (cell, band, tslot?) triples into ONE signed envelope. Each triple runs through the standard auto-materialize recall path; the resulting fact_cids are bundled into a content-addressed envelope and the responder signs over the full receipt. The composed `bundle_token` is `memb:<bundle_cid>` — a single rebindable string that cites the whole set.
  readL0
• Dereference a memory_bundle token
  Parse a `memb:<bundle_cid>` token and return the signed bundle envelope: every citation (cell, band, resolved_tslot, fact_cid, memory_token), the receipt, the responder pubkey, and the deduped flat cells[] / fact_cids[] arrays. Returns 404 with a typed code when the responder does not hold the bundle.
  readL0
• memory_view — read file or directory listing
  Read the contents of a memory file at `/memories/<path>` or list a directory when the path ends with `/`. Optional `view_range: [start, end]` slices a 1-indexed inclusive line range out of the file. Mirrors the `view` verb in Anthropic's context-management-2025-06-27 memory tool spec.
  readL0
• memory_create — write a memory file (overwrite if exists)
  Write a memory file at `/memories/<path>` with the supplied `file_text`. Overwrites if the file exists. Persists to sled, content-addresses the bytes (`file_cid`), and signs the write so the operation carries a verifiable receipt. Mirrors the `create` verb in Anthropic's context-management-2025-06-27 memory tool spec.
  writeL0
• memory_str_replace — exact-string replacement in a memory file
  Replace `old_str` with `new_str` in the named memory file. Fails (no partial write) when `old_str` is absent or matches more than once. Writes a new content-addressed `file_cid` and signs the receipt. Mirrors the `str_replace` verb in Anthropic's context-management-2025-06-27 memory tool spec.
  writeL0
• memory_insert — insert at a given line
  Insert `new_str` after the given 1-indexed line in the named memory file. `insert_line: 0` inserts at the top. Writes a new `file_cid` and signs the receipt. Mirrors the `insert` verb in Anthropic's context-management-2025-06-27 memory tool spec.
  writeL0
• memory_delete — remove a memory file or directory
  Delete a memory file at `/memories/<path>`. When the path ends with `/`, every file beneath the directory is removed. Updates the path index but leaves prior content-addressed blobs in place (the audit history is append-only). Mirrors the `delete` verb in Anthropic's context-management-2025-06-27 memory tool spec.
  writeL0
• memory_rename — move a memory file
  Move (rename) a memory file from `old_path` to `new_path`. Both paths must stay under `/memories/`; `new_path` must not already exist. The file_cid is preserved (no re-sign) so the prior receipt still binds the bytes. Mirrors the `rename` verb in Anthropic's context-management-2025-06-27 memory tool spec.
  writeL0
• memory_list_by_kind — typed enumeration of memory files
  List memory files by their typed `kind` (episodic | semantic | procedural | resource). Optional path prefix narrows the scan; results are sorted by signed_at descending. The kind taxonomy follows the CoALA / LangMem / MIRIX agent-memory ontology: `episodic` = observations of events, `semantic` = durable learned facts, `procedural` = playbooks, `resource` = generic durable scratchpad (default for back-compat).
  readL0
• emem_memory_search — semantic search over /memories/* files
  Semantic search over /memories/* file contents using BGE-base-en-v1.5 (768-D, L2-normalised) backed by a Lance partition (`memory_text_index_d768.lance`). Matches paraphrases — "rainfall in March" finds "precipitation observed in spring" without an exact substring match. Returns ranked hits with similarity in [0,1], 200-char snippets around the best-matching chunk, and the signing receipt's path / file_cid / signed_at / attester_pubkey_b32 fields. Filters: `kind`, `path_prefix`, `attester_pubkey_b32`. Falls back to a brute-force scan (slower but correct) when the index is empty or `EMEM_DISABLE_LANCE=1` is set; the `via` field of the response reports which path was taken.
  readL0
• Signed snapshot of corpus liveness
  Signed snapshot of corpus liveness: distinct_cells, distinct_bands, facts_scanned, top per-band counts, manifest CIDs. Same payload that backs /v1/stream's corpus.state tick (signed). Use this for a one-shot poll instead of holding an SSE connection.
  readL0
• Hand-verified eval items for agent grading
  Hand-verified evaluation items for grading an agent against the responder. Returns {items[], grader_url}. Submit answers (cell64 or fact_cid per item) to POST /v1/benchmark/grade for per-item scores. Items today: elevation recall, NDVI, find_similar neighbours.
  readL0
• Recall facts at a cell (auto-materializes on miss)
  Recall facts about a cell — auto-materializes on miss for any band with a registered materializer.
  readL0
• Recall facts across a place's polygon
  Recall facts across every cell inside a place's polygon (single signed envelope). Closes the place-name-drift gap for wide features (parks, lakes, regions).
  readL0
• Per-field agricultural boundaries (Fields of The World)
  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).
  readL0
• Aggregate facts over a region
  Query facts over a region (single cell or list of cells), optionally aggregated per band.
  readL0
• Compare two cells (cosine + scalar deltas)
  Compare two cells: cosine similarity over shared vector bands + per-band scalar deltas.
  readL0
• Compare two bands at one cell
  Compare two bands at the same cell. Scalar pair → metric=delta, value=b-a. Vector pair (equal dim) → metric=cosine + per-dim delta. Returns a signed receipt naming both source fact CIDs.
  readL0
• k-NN over the corpus by embedding
  k-NN over the corpus by cell embedding or inline vector.
  readL0
• Time series for one (cell, band)
  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`.
  readL0
• Signed delta between two tslots
  Compute a DerivativeFact (delta) between a band's values at two tslots.
  readL0
• Scan for multi-attester disagreement
  Surface where the corpus DISAGREES with itself. When two or more independent sources signed different values for the same place + band + time, this returns that disagreement with a 0–1 severity score and citations to every disputed fact — instead of silently picking one value and hiding the conflict. The opposite of a confident single answer: it tells you when not to trust one.
  readL0
• Recall temporal knowledge-graph edges
  Read temporal knowledge-graph edges (subj --pred--> obj, valid over [valid_from, valid_to)), bi-temporally filtered, in EITHER direction. Forward (`subj`, direction="out", the default): edges originating at a subject fact. Reverse (`obj`, direction="in"): edges pointing AT a fact — what disagrees-with / supersedes / relates-to it. Returns a signed list of edges plus the distinct neighbour fact CIDs (`objs` for out, `subjs` for in); the receipt commits the returned edge CIDs into its signature preimage.
  readL0
• Resolve a fact by content-address (CID)
  Fetch a fact by its content-address (CID). Returns the full signed Primary or Absence fact — the same body served by REST `/v1/facts/{cid}`. Closes the citation loop: any fact_cid surfaced by recall, materialize, attest, or verify can be re-resolved by another agent without REST.
  readL0
• Materialize historical facts in a window
  Materialize and sign every per-tslot fact for one (cell, band) inside a [start_unix, end_unix] window. Returns a signed list of (tslot, fact_cid, status) for each step. Slow but possible — one upstream fetch per tslot, capped by `max_facts`.
  readL0
• 2-D heat-equation forecast (urban LST evolution)
  Forward-step 2-D explicit finite-difference solver for the heat equation ∂u/∂t = α∇²u over a 3×3 cell stencil centred on `cell`. Reads `modis.lst_day_8day` (Land Surface Temperature) at the centre and 8 cell64 neighbours, integrates N hours ahead under a CFL-stable timestep, returns a signed forecast. Real PDE rollout — not a decay-scoring heuristic.
  readL0
• 1-D shallow-water swell propagation to coast
  Forward-step 1-D explicit finite-difference solver for the shallow-water wave equation ∂²u/∂t² = c²∂²u/∂x² with c² = g·h, where depth h comes from `gmrt.topobathy_mean` along the seaward gradient. Models how an offshore swell of height H_s and period T propagates toward `coastal_cell`. Returns arrival height + time + depth + phase-speed profiles, all under a CFL-stable timestep.
  readL0
• Constrained JEPA-pattern next-month NDVI predictor
  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.
  readL0
• Learned multi-band-scalar dynamics head (jepa_temporal_predictor@2)
  Predict the next-step value of 4 environmental scalars at a cell — `indices.ndvi`, `modis.lst_day_8day`, `modis.lst_night_8day`, `cams.pm25` — using a small learned dynamics MLP. Reads up to K=6 most-recent attested lags per band, runs them through an ONNX dynamics head (~200k params, CPU-fast), and returns a per-band {value, confidence, n_real_lags, via}. The receipt's `model` block carries `model_id`, `version`, `blake2b_hex` (model_cid), training/validation provenance, a top-level `skill_vs_persistence` block, and `honesty_warnings` — flagging `untrained_baseline` when the artifact is the zero-init sentinel and `NEGATIVE_SKILL` when the learned model is worse than persistence on real held-out NDVI. When the model does not beat persistence, bands with a real lag are returned from that lag tagged `via:persistence_fallback_negative_skill` (bands with no real lag fall back to labelled climatology). Distinct from v1 (`emem_jepa_predict`) which returns a single NDVI scalar via closed-form coefficients.
  readL0
• Verify a structured claim against a cell
  Verify a structured claim against a cell's facts. Returns verdict + evidence CIDs + signed receipt.
  verifyL1
• Active band ontology
  Active band ontology (offsets, dims, tempo, privacy).
  introspectL0
• Active function registry
  Active function registry (derivation recipes).
  introspectL0
• Active source-connector registry
  Active source-connector registry (URL templates, providers, licenses).
  introspectL0
• Active CDDL/JSON schema bundle
  Active CDDL/JSON schema bundle by CID.
  introspectL0
• Stable error code catalog
  Stable error code catalog.
  introspectL0
• Active manifest CIDs
  Active manifest CIDs (bands / functions / sources / schema).
  introspectL0
• Cached upstream capability snapshot
  Live capability snapshot of the responder's GPU sidecar — extensions[] (e.g. gpu, clay-v1.5, prithvi-eo2), cuda_available, models_loaded[], healthy, last_polled_unix_s. Refreshed every 30 s by a background poller; reads are constant-time.
  introspectL0
• Active grid encoding
  Active grid encoding: cell64 ground resolution, lat/lng axis sizes, DGGS lineage.
  introspectL0
• Per-band live status & history bounds
  Per-band live status — what data is alive AND auto-materializable, with history bounds, tempo cadence, and the responder pubkey that signs the band.
  introspectL0
• Auto-fetch registry (per-band materializers)
  Auto-fetch registry: which bands the responder will materialize on a recall miss, the upstream provider, license, value shape, and history bounds.
  introspectL0
• Per-band temporal coverage catalog
  Temporal catalog: for every materializable band the upstream-of-record window the data genuinely covers, the temporal `kind` (static | annual_snapshot | annual_stack | time_series | now_only | per_release), tempo seconds, upstream wire path, and whether `emem_backfill` is meaningful.
  introspectL0
• Composition recipes (algorithms)
  Content-addressed dictionary of composition recipes — formulas that fuse attested band facts (and embeddings) into derived scores, classifications, and similarity metrics.
  introspectL0
• One-algorithm drill-down (formula + inputs + citation)
  Per-key drill-down on a single composition recipe — full body (kind, inputs, formula, output, citation, references) for ONE algorithm key. Companion to `emem_algorithms` (which is the catalog).
  introspectL0
• Topic-grouped band + algorithm registry
  Topic-grouped registry of every band and algorithm at this responder, plus visual surfaces and the `declared_but_no_materializer_at_this_responder` block (cube slots reserved without a live connector). Single source of truth shared with `/v1/locate`'s `data_at_this_cell` block.
  introspectL0
• Coverage map (SVG image)
  Live SVG render of the responder's corpus density, returned as a proper MCP EmbeddedResource content block (image/svg+xml) — multimodal MCP agents can render it natively.
  introspectL0
• Sentinel-2 true-colour thumbnail (PNG)
  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.
  readL0
• Cell polygon as GeoJSON
  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.
  readL0
• Bulk recall across up to 256 cells
  Recall facts across a list of up to 256 cell64 strings in one signed envelope. Server fans out per-cell recalls in parallel, then aggregates the response. Auto-materializes any cell with a missing fact whose band has a registered materializer — same contract as emem_recall.
  readL0
• Coherent elevation across Cop-DEM + GMRT + WorldCover
  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.
  readL0
• Satellite / sensor lineage per band
  Per-band satellite-and-sensor fleet inventory — names the upstream platform (e.g. Sentinel-2A/B, MODIS Aqua/Terra, Landsat-8/9), revisit cadence, native resolution, and license for every materialized band. Lets an agent attribute imagery products correctly and pick the right band when revisit cadence matters.
  introspectL0
• Plan a temporal recall recipe for a cell
  Turn a time-shaped question into a ready-to-run recall plan: it figures out WHICH bands to pull at WHICH past time windows (e.g. 'the year before the flood', 'last growing season', 'two vintages to compare') so you don't have to compute tslot offsets by hand. Returns the band + lookback + a `purpose` tag for each step.
  planL0
• Server-side ed25519 receipt verifier
  Verify a signed receipt envelope server-side: recomputes the canonical preimage (`request_id | served_at | primitive | cells, | fact_cids,`), runs ed25519 over the embedded pubkey + signature, and returns `{valid, reason, pubkey_b32}`. Use when the in-browser /verify path is blocked (CDN offline, agent runtime has no crypto) or when you want a server-side audit of a third-party receipt.
  verifyL1
• Multi-band snapshot at a place
  One-shot multi-band recall at a place (or lat/lng). Defaults to emem's standard at-a-glance band set; pass `band` / `bands` to override. Polygon-resolved places stay at the centroid by default (`n_cells: 1`) to keep multi-band calls cheap — pass `n_cells: 2..=64` to fan out.
  readL0
• NDVI at a place (one-shot, polygon-aware)
  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.
  readL0
• Air-quality snapshot (CAMS PM2.5 / NO2 / O3)
  Recall Copernicus CAMS air-quality bands at a place: PM2.5 + NO2 + O3. Composes locate → recall → aggregate.
  readL0
• Land surface temperature (MODIS day + night)
  Recall MODIS land surface temperature day-8day + night-8day composites at a place. 1 km native, 8-day composite.
  readL0
• Soil profile (SoilGrids 0–30 cm: SOC, pH, texture)
  Recall SoilGrids 250 m profile at a place: SOC, pH, clay/sand/silt fractions, bulk density, nitrogen — all at 0–30 cm depth.
  readL0
• Surface water (JRC GSW recurrence + S1 backscatter)
  Recall surface-water signals at a place: JRC Global Surface Water recurrence (1984–2021) + Sentinel-1 SAR backscatter (current). Pair detects standing water through clouds.
  readL0
• Forest signals (Hansen GFC + ESA WorldCover)
  Recall forest signals at a place: Hansen Global Forest Change (tree cover 2000 baseline + year-of-loss) + ESA WorldCover 2021 land class.
  readL0
• Current weather snapshot (temperature, cloud, precip, wind)
  Recall the standard met.no/CAMS weather bundle at a place: 2 m temperature + total cloud cover + precipitation + 10 m wind speed.
  readL0
• Intent-routed planner
  Submit a typed Intent; receive a plan or executed result.
  planL0

How to call

https://emem.dev/mcp