emem — Earth memory protocol

skills: {'id': 'emem_locate', 'name': 'emem_locate', 'description': "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.\n\nWhen to use: Use whenever the input refers to a real-world location and the next step needs the cell64 identifier or wants to know which bands are available before recalling. The response carries `data_at_this_cell` with three sub-fields: `live_bands_by_topic` (every band recallable here, grouped by topic such as flood_water_event_window, vegetation_condition, built_up_human_geography), `algorithms_for_topic` (composition recipes that fuse those bands into named scores), and `declared_but_no_materializer_at_this_responder` (cube slots reserved without a live connector). For the single-shot path that runs the full chain server-side and returns one packaged answer, use `emem_ask` instead.", 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}, {'id': 'emem_ask', 'name': 'emem_ask', 'description': "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.\n\nWhen to use: Use when the question concerns a specific real-world place and a packaged, citation-bearing answer is preferable to manual primitive composition. Forward the user's question verbatim as `q` plus the location as `place` (free text), `cell` (cell64), or `lat`+`lng`. The server resolves the location, classifies the question to a topic, recalls every relevant band (auto-materializing Sentinel-2 / Sentinel-1 / Cop-DEM / JRC GSW / Overture / weather on miss), surfaces the algorithm recipes that compose those bands into named scores, and returns a single envelope with `topic_routing`, `facts`, `algorithms_for_question`, an optional Sentinel-2 RGB scene URL, and a `caveats` block (grid resolution, revisit cadence). All facts are signed by the responder; the signed `receipt` (and its content-addressed `fact_cids`) is surfaced at the envelope ROOT — `response.receipt` / `response.fact_cids` — exactly like every other primitive, and is also mirrored under `facts_summary.receipt` for back-compat. Set `include_image: true` to bundle the latest cloud-free Sentinel-2 thumbnail. Out-of-scope questions return `topic_routing.matched_topic: null` plus the full inventory so the caller can route elsewhere.", 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}, {'id': 'emem_hunt', 'name': 'emem_hunt', 'description': '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).\n\nWhen 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.', 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}, {'id': 'emem_eudr_dds', 'name': 'emem_eudr_dds', 'description': "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.\n\nWhen 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).", 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}, {'id': 'emem_spi', 'name': 'emem_spi', 'description': "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.\n\nWhen to use: Call when the user asks 'is this place in drought', 'how dry is it relative to normal', or wants a precipitation-anomaly z-score. The response is honest: when fewer than the WMO-recommended minimum samples exist it returns verdict=`inconclusive` with `spi:null` and a `honest_note` rather than fabricating a z-score from a handful of points. Quote the `spi`, `spi_class`, and `n_samples`. For raw precipitation use `emem_weather`; SPI is the standardized anomaly.", 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}, {'id': 'emem_burn_severity', 'name': 'emem_burn_severity', 'description': '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.\n\nWhen to use: Call after a wildfire to quantify how badly an area burned, or to triage post-fire severity across a region cell-by-cell. Best practice: explicitly pass `nbr_pre`/`nbr_post` from scenes that bracket the known fire date — the stored-trajectory fallback just takes the two most-recent scenes and may not bracket the fire. Surface `dnbr` and `severity_class`. For active-fire detection use `emem_hunt` with the wildfire event instead.', 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}, {'id': 'emem_rice_ch4', 'name': 'emem_rice_ch4', 'description': "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.\n\nWhen to use: Call for paddy-rice GHG inventory / MRV work where the user needs kg CH4 per hectare for a cultivation season. The caller MUST pick the IPCC region's EFc row (Table 5.11) and the cultivation-period length; pass SFp/SFo when the water regime or organic amendment is known. Surface the seasonal emission, the EFc used, and the scaling factors so the inventory is auditable. For enteric/fertilizer pathways use the dedicated sustainability endpoints.", 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}, {'id': 'emem_deforestation_alert', 'name': 'emem_deforestation_alert', 'description': "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.\n\nWhen to use: Call to flag recent forest-loss-like change at a known cell when you want a single 0..1 alert score rather than a full ensemble. Read the renamed score field and the present/absent halves — don't treat a half-score as the full composite. For multi-cell open-world discovery use `emem_hunt` (deforestation event); for the three-encoder change ensemble use `emem_triple_consensus`; for regulatory EUDR evidence use `emem_eudr_dds`.", 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}; uptime_30d 1.0%; p95 1561.4ms; conformance: pass

How to connect

https://emem.dev/mcp

curl -X POST https://emem.dev/mcp \
  -H 'Content-Type: application/json' \
  -H 'Accept: application/json, text/event-stream' \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{}}'