Skip to main content
Glama
253,695 tools. Last updated 2026-07-01 01:24

"namespace:io.github.team-earth" matching MCP tools:

  • 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. When to use: Call when the user wants cross-source consistency at one place ('does Cop-DEM agree with GMRT here?'), cross-vintage drift ('how did the embedding change between 2017 and 2024 at this cell?'), or any band-vs-band comparison within a single cell. `cell` + `a` + `b` are required. `tslot_a`/`tslot_b` are OPTIONAL: omit them to let the responder auto-pick each band's latest attested tslot — required for medium/fast-tempo bands (NDVI 30-day, MODIS 8-day, weather, CAMS) where there is no fact at tslot=0. The response carries `tslot_resolution` (echoes what was chosen and why) and `bands_with_no_history` (lists any band the cell has no attested fact for).
    Connector
  • Returns all 14 cards in a given Minor Arcana suit as a structured array. SECTION: WHAT THIS TOOL COVERS Each of the four suits has 14 cards: Ace through 10 plus Page, Knight, Queen, King. Elemental associations: Wands=fire (action, career, creativity), Cups=water (emotions, relationships, intuition), Swords=air (intellect, conflict, truth), Pentacles=earth (material, money, body, practical matters). SECTION: WORKFLOW BEFORE: None — standalone. AFTER: None. SECTION: INPUT CONTRACT suit — One of exactly: 'wands', 'cups', 'swords', 'pentacles'. Case-insensitive. Any other value is rejected locally with MCP INVALID_PARAMS. SECTION: OUTPUT CONTRACT data[] — 14 card objects for the requested suit, each identical to asterwise_get_tarot_card output. Ordered Ace through King. SECTION: RESPONSE FORMAT response_format=json — array of 14 card objects. response_format=markdown — formatted list. SECTION: COMPUTE CLASS FAST_LOOKUP SECTION: ERROR CONTRACT INVALID_PARAMS (local): — suit not in {wands, cups, swords, pentacles} → MCP INVALID_PARAMS immediately. INTERNAL_ERROR: Any upstream API failure → MCP INTERNAL_ERROR SECTION: DO NOT CONFUSE WITH asterwise_get_tarot_major_arcana — 22 Major Arcana, not suit-based. asterwise_get_tarot_cards — full 78-card catalogue.
    Connector
  • 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.
    Connector
  • 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. When 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.
    Connector
  • 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. When to use: Call when the user asks how steep / how rugged / ridge-or-valley a place is, for siting (solar, construction, agriculture), erosion/landslide screening, or habitat-heterogeneity inputs. Slope and TRI need the full 8-neighbour ring; TPI degrades to ≥1 neighbour. Copernicus DEM is bathymetry-free, so ocean cells return a signed `inconclusive` rather than a fabricated slope — read each index's own `verdict`. For raw elevation use `emem_elevation`.
    Connector
  • 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. When to use: Call this when trust matters before you rely on a number — 'is there disagreement about X', 'do the sources corroborate this', 'audit this claim', or 'find contradictory observations in region Y'. Use it to decide whether a fact is well-corroborated or contested. Narrow with `cell_prefix` (e.g. "defi.zb5") for a region and `band` for one family; `min_severity` filters out trivial differences. Severity is per band kind: scalar = spread over the band's range, vector = 1 − mean cosine, categorical = 1 − mode share. The receipt cites every disputed CID — follow up with `emem_diff` to quantify a pair, or (with the refinement loop on) read the emitted `disagrees_with` edge via `emem_edges_recall`.
    Connector

Matching MCP Servers

Matching MCP Connectors

  • Content-addressed, ed25519-signed memory of every place on Earth. Apache-2.0, no keys for reads.

  • Screens public GitHub repos and PRs to generate risk maps, findings, and merge-readiness signals.

  • 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). When to use: Call to corroborate or scout forest clearing where cloud blocks the optical products — radar sees through cloud and at night, catching wet-season clearing the annual Hansen/JRC-TMF layers and a single cloudy Sentinel-2 pass miss (the gap RADD was meant to fill). This is an ADDITIVE scout signal, NOT a standalone legal verdict: a VV drop can also be transient (soil moisture, harvest, flood recession), so confirm with the optical consensus (`emem_eudr_dds` or `emem_deforestation_alert`) before crediting a decision.
    Connector
  • Per-band live status — what data is alive AND auto-materializable, with history bounds, tempo cadence, and the responder pubkey that signs the band. When to use: Call BEFORE `emem_recall` when you don't know which bands answer at this responder. For each band returns `has_materializer` (true → an empty recall will auto-fetch+sign, no seeding needed), `facts_count` (how many cells already cached), `last_attested_unix_s` (freshness), `tempo_seconds` (slot duration), `history_available_from` / `history_available_to` (oldest/newest Unix epoch the materializer can fetch — use these to bound an `emem_backfill` request), and `responder_pubkey_b32` (the ed25519 key whose signature attests this band — use to detect federation / multi-responder setups). Bands with `has_materializer=false AND facts_count=0` are cube placeholders without a wired connector — don't bother recalling them.
    Connector
  • Calculate a complete Western natal chart using the tropical zodiac and Swiss Ephemeris. Returns 10 planet positions with Placidus (or chosen) house placements, essential dignities, all active aspects, and element/modality/hemisphere balance statistics. SECTION: WHAT THIS TOOL COVERS Tropical natal chart: Sun, Moon, Mercury, Venus, Mars, Jupiter, Saturn, Uranus, Neptune, Pluto. Each planet returns tropical longitude, sign, house (1–12), retrograde flag, dignity label (domicile/exaltation/detriment/fall/peregrine), dignity score (domicile +5, exaltation +4, triplicity +3, term +2, face +1, detriment -5, fall -4), is_exaltation_degree (within 1° of exact exaltation), dignity_disputed (true for outer planets where exaltation/fall is disputed among modern astrologers). Aspect orbs: conjunction/opposition 5°, square/trine 5°, sextile 3°, minor aspects 1.5°. Not Vedic sidereal (asterwise_get_natal_chart). SECTION: WORKFLOW BEFORE: None — this tool is standalone. AFTER: asterwise_get_western_transits_daily — layer current transits over this natal chart. AFTER: asterwise_get_western_synastry — compare this chart against a partner's chart. AFTER: asterwise_get_western_solar_return — annual return chart for the current year. SECTION: INPUT CONTRACT birth.date — YYYY-MM-DD. Example: '1985-11-12' birth.time — HH:MM (24-hour local time). Example: '06:45' birth.lat — Decimal degrees, north positive. Example: 19.076 (Mumbai) birth.lon — Decimal degrees, east positive. Example: 72.8777 (Mumbai) birth.timezone — IANA timezone string. Example: 'Asia/Kolkata', 'America/New_York', 'Europe/Rome', 'UTC'. Default: UTC. IMPORTANT: Timezone defaults to UTC — always supply the correct local timezone for accurate house cusps. An incorrect timezone shifts the Ascendant. birth.house_system — 'placidus' (default, most common), 'koch', 'equal', 'whole_sign'. Placidus is standard for most Western traditions. Whole sign is traditional/Hellenistic. NOTE: house_system is accepted here but silently ignored by transit, return, synastry, composite, and progression endpoints — those always use the birth location coordinates without house-system selection. ayanamsa — always tropical regardless of any value supplied; field is not present. SECTION: OUTPUT CONTRACT data.zodiac (string — 'tropical') data.house_system (string — the system used) data.ascendant — { longitude (float), sign (string), sign_index (int 0–11), degree_in_sign (float) } data.mc — same shape as ascendant data.planets[] — 10 objects (Sun through Pluto): name (string), longitude (float), sign (string), sign_index (int 0–11) degree_in_sign (float), house (int 1–12) is_retrograde (bool), dignity (string), dignity_score (int) is_exaltation_degree (bool), dignity_disputed (bool) data.houses[] — 12 objects: house (int 1–12), cusp_longitude (float), sign (string) sign_index (int 0–11), degree_in_sign (float) data.aspects[] — each: planet_a (string), planet_b (string), type (string) exact_angle (float), orb (float), is_applying (bool) data.elements — { fire (int), earth (int), air (int), water (int), dominant (string) } data.modalities — { cardinal (int), fixed (int), mutable (int), dominant (string) } data.hemisphere — { eastern (int), western (int), northern (int), southern (int) } data.ayanamsa_value (float — 0.0 for tropical) data.ayanamsa_used (string — 'tropical') data.birth_time_provided (bool) 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 natal report. Both modes return identical underlying data. SECTION: COMPUTE CLASS MEDIUM_COMPUTE (~300ms) SECTION: ERROR CONTRACT INVALID_PARAMS (local — caught before upstream call): — WesternBirthData Pydantic violations (date pattern, time pattern, lat/lon bounds) → MCP INVALID_PARAMS INVALID_PARAMS (upstream): — None expected for valid coordinates and dates post-1800. INTERNAL_ERROR: — Any upstream API failure or timeout → MCP INTERNAL_ERROR Edge cases: — Polar latitudes (above ~65°N or below ~65°S) may cause Placidus house calculation failure; use whole_sign or equal house system for polar births. — time='00:00' accepted; lagna-sensitive results are unreliable for unknown birth times. SECTION: DO NOT CONFUSE WITH asterwise_get_natal_chart — Vedic sidereal chart using Lahiri ayanamsa; different zodiac, different house system, different planet set (9 grahas vs 10 tropical planets). asterwise_get_western_aspects — takes raw longitudes as input; use when you already have positions and don't need full chart computation.
    Connector
  • Sign-to-sign compatibility without birth data. Based on element and modality affinity. Fast — no ephemeris calculation required. SECTION: WHAT THIS TOOL COVERS Lookup table compatibility using sign elements (fire/earth/air/water) and modalities (cardinal/fixed/mutable). No houses, no Moon phase, no Venus Mars geometry. SECTION: WORKFLOW BEFORE: None — no birth data needed. AFTER: asterwise_get_western_compatibility — when full charts are available. SECTION: INPUT CONTRACT sign1, sign2 — English zodiac names (Aries … Pisces). SECTION: OUTPUT CONTRACT data.sign1, data.sign2 data.element1, data.element2 data.modality1, data.modality2 data.element_affinity, data.modality_affinity — 'harmonious'|'neutral'|'challenging' data.overall_score (int 0-100) data.description (string) SECTION: RESPONSE FORMAT response_format=json serialises the complete response as indented JSON. response_format=markdown renders the same data as a human-readable report. Both modes return identical underlying data. SECTION: COMPUTE CLASS FAST_LOOKUP — no ephemeris, pure table lookup. SECTION: ERROR CONTRACT INVALID_PARAMS (local): None — sign validation upstream. INTERNAL_ERROR: Any upstream API failure → MCP INTERNAL_ERROR SECTION: DO NOT CONFUSE WITH asterwise_get_western_compatibility — requires full birth data, more accurate. asterwise_get_western_synastry — aspect geometry between two full charts.
    Connector
  • 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. When to use: Use when the user wants a short-horizon LST forecast (urban heat island, surface-temperature evolution, heatwave onset modelling) at a specific cell. Default α=1e-6 m²/s matches urban surface diffusivity (Oke 2017); pass a smaller α for water bodies or higher for vegetated surfaces. The solver caps at one-week horizons because the 8-day MODIS composite stops being a representative initial condition past that. Each call materialises 9 MODIS facts (one per neighbour) on miss — first call ~5 s cold, ~30 ms warm. Receipt cites all 9 input fact CIDs.
    Connector
  • 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. When to use: Call before scheduling a GPU-heavy plan (Clay / Prithvi / Galileo embeddings, foundation-anchored algorithms) so the agent knows whether the GPU tier is up *right now* without per-request /health round-trips. Pair with `emem_topics` (its `algorithm_availability` map says which algorithm keys can run given the current capabilities) and `emem_explain_algorithm` (full inference-tier metadata per algorithm). When `extensions[]` is empty the sidecar is unreachable — only CPU/scalar/cached tiers will produce facts; foundation-anchored materializers will sign Absence with `gpu_unavailable` reason.
    Connector
  • 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.
    Connector
  • 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. 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).
    Connector
  • 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. When to use: Call when an agent needs a single liveness reading to surface in a dashboard, attach to a report, or decide whether to refresh local caches. Includes ed25519 signature over a deterministic preimage so the snapshot is verifiable. For a continuous feed, GET /v1/stream over Server-Sent Events instead.
    Connector
  • 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. When 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.
    Connector
  • 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). When to use: Call to compare two areas at the level of overall land character (e.g. 'is this valley like that one?', 'find me somewhere that looks like X'). Degrades to a signed `inconclusive` (no number) when a region has no embedding-covered cells. For a single cell-to-cell vector cosine use `emem_compare`; for k-NN retrieval use `emem_find_similar`.
    Connector
  • 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. When to use: Call when the LLM wants to rename or move a memory file. Failure modes: source missing, destination already exists, path escapes `/memories/`.
    Connector
  • 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. When to use: Call this to read the typed CONNECTIONS of a fact — what disagrees with it, what superseded it, what relates to it — as of a point in time. A plain recall gives you the fact; this gives you how that fact links to others in the memory graph. Ask it when the user says 'what is this related to', 'what replaced this observation', 'why is this value contested', or 'what did this place's relations look like as of date X'. Pick a direction: set `subj` (direction="out") to ask 'what does this fact point at'; set `obj` (direction="in") to ask the REVERSE — 'what disagrees-with / supersedes / points-at this fact'. Set exactly one of subj/obj — an ambiguous or empty request errors honestly rather than returning a silent empty. Pass `as_of_tslot` to get the latest edge per neighbour whose valid interval covers that moment (newer edges shadow older — nothing is deleted); pass `pred` (e.g. `disagrees_with`, `supersedes`) to filter, or omit it (empty string) for every predicate. Tip: a quicker way to get a fact + its outbound edges in one shot is `emem_recall` with include:["edges"]. Follow each edge's `obj`/`subj` with `emem_fetch` to resolve the related fact, or `emem_verify_receipt` to confirm the signature offline.
    Connector
  • Find air-quality monitoring stations (measured by physical sensors, not modeled) near a point, within a bounding box, or by country. Returns each station's id, name, coordinates, distance from the query point (when searching by coordinates), country, provider, the parameters its sensors measure, and the timestamp of its most recent data (datetimeLast). Required first step: openaq_get_readings and openaq_get_measurements key on the location id this returns. Coverage is uneven and real — a station only reports the parameters it measures, and the absence of a nearby station means no monitoring there, not clean air. For dense modeled coverage anywhere on Earth, use open-meteo-mcp-server's air-quality tool instead.
    Connector