Skip to main content
Glama

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
EMEM_BINDNoBind address for the server (default: 0.0.0.0:5051)0.0.0.0:5051
EMEM_DATANoPath to persistent storage directory (default: ./var/emem)./var/emem

Capabilities

Features and capabilities supported by this server

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
completions
{}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
emem_toolsA

The map of emem's tool surface, and the only tool you need to find the rest. Returns the working loop in the order you walk it (name a thing, ground it, cite it, resolve it, verify it, check for drift), then every other tool grouped by the question it answers, each with its one-line trigger. Pass name to get one tool's full input schema and a runnable example, so you can use a tool without loading all of the descriptors into context. This endpoint advertises the core loop only; the Earth-observation, search, embedding and log tools are catalogued here and remain callable by name.

When to use: Call this FIRST when you do not know which emem tool answers the question, or when you need a capability you cannot see in your tool list. This responder advertises a small core loop by default rather than its full catalog, so a tool being absent from your list does not mean it is absent from the server. Pass q to search by topic (ndvi, cloud, flood, verify), name for one tool's exact schema, or no arguments for the whole map. If you want the full catalog registered as callable tools instead, reconnect to the /mcp/full endpoint; for a one-shot answer without picking a primitive at all, use emem_ask.

Example arguments: {"q":"ndvi"}

emem_locateA

Mint the canonical, vendor-neutral address (cell64) for a real-world place: the shared spatial identity every agent resolves to identically, so two models refer to the same ground instead of two descriptions of it. Also returns the topic-grouped inventory of bands and algorithms recallable there. For a first-class OBJECT identity (a bridge, a plot, a named place) rather than a raw cell, use emem_entity. Send EITHER lat+lng as numbers OR a free-text place; coordinates win when both arrive. q, query and name are all accepted spellings of place. A key this schema does not declare is reported in _unrecognised_arguments, so a typo answers about somewhere else rather than erroring.

When 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.

Example arguments: {"place":"Mount Everest"}

emem_askA

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.

When 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.

Example arguments: {"q":"is this neighbourhood flood-prone for a flat purchase","place":"Ashok Nagar, Ranchi"}

emem_memory_tokenA

Mint a citation handle, emem:fact:<cell64>:<fact_cid> (or :<state_cid>), that any agent or LLM resolves to the byte-identical signed object. The antidote to referential drift on the value side: hand this one string to another agent instead of re-describing the fact. Validates both components are non-empty and free of the : separator. Memory algebra: the cite operation (https://emem.dev/docs/model.html).

When to use: Call when the agent wants a single rebindable string to cite a place plus an attested fact across messages, threads, agents, or tools, without re-fetching or re-describing it. Pair with emem_verify_receipt on the receiving end to check the signed payload. To cite an OBJECT rather than a single reading, use emem_entity's emem:entity: token. FOR MANY FACTS, USE emem_memory_bundle INSTEAD, and this is a measured cost rather than a style preference: a token is ~104 characters while the signed value it points at averages ~18, so N individual tokens cost roughly 5.8x the context of simply pasting the N numbers, and an N-token prompt hits the context wall SOONER than the plain values would. A bundle is 38 characters at ANY N up to 256 and resolves in one round trip. Individual tokens are for citing ONE fact you must be able to verify later; they are the wrong tool for carrying a set.

Example arguments: {"cell":"defi.zb493.xoso.zcb6a","fact_cid":"cxjiu7l54ujzrpnekp24n4534yojpue4mprddbvevnqtti3lh5bq"}

emem_memory_token_resolveA

Parse a emem:fact:<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. Memory algebra: the resolve operation (https://emem.dev/docs/model.html).

When to use: Call when an agent receives a memory_token from another agent (or out of a previous turn) and wants the underlying signed bytes. The response carries the parsed cell + fact_cid, the full fact body, and the stable fact_url an agent can hand to any other peer. 404 with a typed code if the responder doesn't hold the cid; try /v1/fetch with the cid then, or paste the token at a mirror.

Example arguments: {"token":"emem:fact:defi.zb493.xoso.zcb6a:cxjiu7l54ujzrpnekp24n4534yojpue4mprddbvevnqtti3lh5bq"}

emem_echo_verifyA

Grade a value you are about to emit against the signed fact your citation points at. Returns matches and, when it does not, the drift between what you were about to say and what emem holds. This is the step that turns a transcription error into a caught event instead of a silent wrong number: a model that resolves a fact correctly can still retype 0.2411 for 0.241103, and nothing else in the loop notices. Memory algebra: the verify operation (https://emem.dev/docs/model.html).

When to use: Call immediately before publishing, logging, or handing on any value you took from an emem fact, and treat a false matches as a gate rather than a warning. Pair it with value_verbatim from resolve: quote that exact decimal string rather than reformatting the number, then echo-verify what you actually emitted. For a due-diligence or compliance record this is what lets you assert every cited value was echo-verified with a signed check per citation instead of a promise. Accepts a bare cid too, so a damaged citation still grades rather than failing closed.

Example arguments: {"token":"emem:fact:defi.zb572.xoso.zb1ec:2p6sz3pv45ndkyqstir4nd6bjnzx63rrcb4pnhgahsnb2oczh5aq","claimed_value":"-0.0558"}

emem_memory_bundleA

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 emem:bundle:<bundle_cid>, a single rebindable string that cites the whole set. Memory algebra: the merge operation (https://emem.dev/docs/model.html).

When to use: Call when the agent wants to cite multiple (place, band, vintage) facts as one handle. The bundle stays verifiable offline via /v1/verify_receipt (the receipt covers all cited fact_cids and cells). Use this instead of N separate emem_memory_token composers when the citation is conceptually one thing (e.g. "the EUDR-relevant baseline for these 8 plots at 2020-12-31"). Caps at 256 triples per call, and the response reports members and resolved so a bundle that only partly resolved is visible without walking every citation.

Example arguments: {"triples":[{"cell":"defi.zb4d9.pefa.zf619","band":"copdem30m.elevation_mean"},{"cell":"defi.zb493.xoso.zcb6a","band":"indices.ndvi"}],"purpose":"audit baseline 2026"}

emem_entityA

Give a real-world object (a bridge, a farm plot, a river, a named place) a single, shared, content-addressed identity that any agent resolves the same way. Returns an entity_token (emem:entity:<entity_cid>) plus a signed receipt that attests how the reference resolved. Two agents that name the same object mint the SAME entity_cid; when a stable external id (Overture GERS / OSM) is known it dominates identity, so divergent labels for one real object still collapse to one id. This is the object-level antidote to referential drift: 'the damaged bridge near the river' becomes one canonical thing every model reasons about, not a phrase each model re-interprets.

When to use: Call when a conversation refers to a THING and you want a stable handle to it that survives summarization and travels between agents/turns/LLMs, before it drifts into 'that infrastructure issue'. Anchor it with place, a cell, or lat+lng. Hand the returned emem:entity: token to any other agent; they dereference the identical object. Recall/ask at the entity's cell64 for signed facts about it. Pick the right sibling: emem_entity MINTS or returns the identity for a thing you can anchor to a place; emem_entity_resolve takes a fuzzy phrase and finds an identity someone ALREADY registered, so reach for it when you suspect the thing is known and you only have words for it; emem_entity_link asserts that two spellings you already hold mean one object. Do NOT call this for an observation, which is a fact and belongs in emem_recall or emem_memory_token, and do not call it to name a place itself, which is emem_locate: an entity is a THING AT a place, not the place.

Example arguments: {"label":"Golden Gate Bridge","kind":"bridge","place":"Golden Gate Bridge, San Francisco"}

emem_entity_resolveA

Converge a fuzzy phrasing onto the canonical object other agents already minted, so everyone co-refers to the same identity instead of re-minting divergent ones. Pass text (e.g. "the collapsed span at the ford") to get ranked existing candidates; pass near to narrow to a place; or pass an emem:entity: token to dereference it directly to the signed entity body. Read-only.

When to use: Call BEFORE minting when another agent may already have registered the object, or when you receive a emem:entity: token and want the object behind it. This is how two agents avoid referential drift: resolve first, mint only if nothing matches.

Example arguments: {"text":"the golden gate bridge","near":"San Francisco"}

emem_entity_linkA

Record a signed equivalence: bind an alternate label or a stable external id (GERS / OSM / Wikidata) to an existing canonical object so future emem_entity_resolve calls on that phrasing converge to the same entity_cid. Builds the shared reference graph that keeps different agents' vocabularies pointing at one identity.

When to use: Call when you learn that two phrasings denote the same object ('the north dam' == an existing entity), or to attach an authoritative external id to an object minted from free text.

Example arguments: {"entity_token":"emem:entity:0a1b2c3d4e5f60718293","alias":"the north dam"}

emem_recallA

Read the signed facts at a canonical address (cell64); auto-materializes on a miss for any band with a registered materializer. A fact_cid names one signed attestation, so a recalled fact is citeable and re-verifiable rather than a paraphrase: resolving it anywhere returns those exact bytes. It is NOT a fingerprint of the observation. The digest covers the responder's key and the moment it signed, so two responders that measure the same thing mint different fact_cids and a cid resolves only at the responder that signed it; use emem_entity for identity that crosses responders. Pass deterministic:true (or a provenance class list) to keep only facts recomputable from the cited raw source, with no model or human in the loop. In the memory algebra this is ensure(cell, bands), not get: state what must exist and the responder reuses or materializes.

When to use: Call after emem_locate (or with a known cell64). Returns every Primary fact stored at that (cell, band, tslot). IMPORTANT: if the cell has no fact yet for a requested band AND that band has has_materializer=true (per emem_coverage_matrix / emem_materializers), the responder fetches the upstream value, signs it under its identity, persists it, and returns it in the same response (slower on the first call while the upstream is fetched; fast once cached). So for any wired band you can recall ANY cell on Earth without seeding, just pass bands: [<band>]. The response carries materialize_notes listing what was just fetched. Empty result with no notes means the band has no materializer at this responder.

Example arguments: {"cell":"damO.zb000.xUti.zde78","bands":["weather.temperature_2m","copdem30m.elevation_mean"]}

emem_find_similarA

k-NN over the corpus by cell embedding or inline vector. Returns neighbours ordered nearest-first, each with cell64, score and the band scanned, plus a signed receipt over the vectors read. Scoring is mode: cosine is exact fp32; hamming is a sign-bit popcount that scans far more cells for the same budget; hamming_then_rerank does both. k is 1..1000, default 10. It ranks what the corpus already holds and materialises nothing, so an empty result means nobody has attested a vector nearby, not that nowhere resembles the key.

When to use: Call when the user asks 'find places like X', 'where else looks like this', or hands an embedding to find neighbours. key is either a cell64 or inline:[x,y,...]. Default band is geotessera (128-D Tessera foundation embedding); pass band: "geotessera.multi_year" for the 1152-D 9-vintage (2017–2025) fusion.

Example arguments: {"key":"damO.zb000.xUti.zde78","k":10}

emem_memory_contradictionsA

Surface where the corpus DISAGREES with itself (algebra: competing evidence). 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.

Example arguments: {"cell_prefix":"damO","band":"indices.ndvi","min_severity":0.2}

Prompts

Interactive templates invoked by user choice

NameDescription
flood_historyLong-term flood/inundation history at a place, classified from JRC Global Surface Water v1.4 (1984–2021).
air_quality_nowCurrent PM2.5 / PM10 / NO2 / O3 / SO2 / CO at a place, classified against WHO 2021 AQG ladder.
urban_heatUrban-heat-island assessment from MODIS LST day/night and indices.urban_canopy_index, with cooling-potential note.
place_summaryQuick characterisation: landcover (ESA WorldCover), elevation (Cop-DEM), greenness (NDVI), current temperature.
compare_placesCosine similarity over the geotessera 128-D embedding, with dominant-band rationale.
forest_lossHansen Global Forest Change v1.12 layers: tree cover 2000, year of loss (2001–2024), gain mask.
coastal_eutrophicationSDG 14.1.1a coastal-eutrophication first-pass: floating-algae index + chlorophyll proxy + turbidity + SST.
carbon_uptake_anomalyGPP anomaly z-score from MOD17A2H trajectory (current 8-day vs same-DOY climatology).

Resources

Contextual data attached and managed by the client

NameDescription
agents.mdFull integration guide: REST + MCP setup for Claude Desktop/Code, Cursor, Cline, OpenAI GPT, plus tool reference and worked examples.
specAuthoritative protocol spec: cell64, tslot, content-addressing, ed25519 receipts, lazy materialization, attestation merkle root.
arcade-protocolHow an agent joins the live arcade at emem.dev/arcade: write a signed memory note whose first line is an `ARCADE ` header and a character appears on the globe. No roster, no registration. Gives the envelope, paths, every header field, the act vocabulary, the addressed-message form /v1/channel/geo geolocates, and the write preimage to sign. Position is asserted by the writer; attribution is proven by the signature.
whitepaperArchitecture and math: 1792-D voxel layout, BLAKE3 fact CIDs, ed25519 attestation, agent-native invariants.
llms.txtLLM-optimised summary: when to call which primitive, which bands answer which question, 30-second curl examples.
llms-full.txtLong-form LLM-optimised guide: full band catalogue, every primitive with example payload, error catalogue.
agent_walkthroughsAgent walkthroughs: end-to-end flows for flood-history, urban-heat, similarity, and trajectory questions.
temporalTemporal model: Tempo (Static/Slow/Medium/Fast/UltraFast), tslot mapping, backfill semantics, history bounds.
materializersMaterializer playbook: how a band's upstream connector turns into a signed fact, error envelope, no-fallback rule.
agent.jsonDiscovery manifest: operator, surfaces, primitives, policies, what a host platform reads to wire emem in.
privacyPrivacy policy of the responder operator (Vortx AI). No PII collected from inbound MCP/REST requests.
termsTerms of service of the responder operator. Apache-2.0 protocol; hosted-instance terms.
registry.bandsFull bands manifest keyed by family/topic with units, value_range, interpretation, and per-band dimensions.
registry.algorithmsAlgorithm registry: formula text, inputs, evaluation AST, accuracy_band, temporal_recipe, citations.
registry.sourcesUpstream source registry: connector wiring, license, attribution, revisit cadence per source.
registry.topicsTopic taxonomy: which bands/algorithms answer which natural-language question.
registry.functionsFunction registry: per-band recipes that turn raw upstream sources into one signed fact.
registry.schemaActive CDDL/JSON schema bundle, describes Receipt, Fact, RecallResp shapes on the wire.
corpus.state_statsSigned snapshot of corpus liveness: distinct_cells, distinct_bands, facts_scanned, top per-band counts.

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Vortx-AI/emem'

If you have feedback or need assistance with the MCP directory API, please join our Discord server