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 retu...
Part of the emem — Earth memory protocol server.
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AI agents call emem_jepa_predict_v2 to retrieve information from emem — Earth memory protocol without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.
Even though emem_jepa_predict_v2 only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.
Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.
{
"version": "1",
"default": "deny",
"tools": {
"emem_jepa_predict_v2": {}
}
}See the full emem — Earth memory protocol policy for all 81 tools.
These attack patterns abuse exactly the kind of access emem_jepa_predict_v2 gives an agent. Each links to the full case and the policy that stops it:
Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.
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. When to use: Use when you want a short-horizon forecast of NDVI / land-surface temperature / PM2.5 at a cell grounded in its attested history. Returns 422 with a /v1/backfill hint when the cell lacks enough cached lags. Always read the receipt's model.honesty_warnings — untrained_baseline means the trivial 'predict last vintage' baseline (treat as no-op), and NEGATIVE_SKILL means the served values are the persistence fallback, not a learned improvement. Check each band's via field to see whether its value came from the learned model, persistence, or climatology.. It is categorised as a Read tool in the emem — Earth memory protocol MCP Server, which means it retrieves data without modifying state.
Register the emem — Earth memory protocol MCP server in PolicyLayer and add a rule for emem_jepa_predict_v2: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches emem — Earth memory protocol. Nothing to install.
emem_jepa_predict_v2 is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the emem_jepa_predict_v2 rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for emem_jepa_predict_v2. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
emem_jepa_predict_v2 is provided by the emem — Earth memory protocol MCP server (oci:ghcr.io/vortx-ai/emem:latest). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 81 emem — Earth memory protocol tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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