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 concatenat...
Part of the emem — Earth memory protocol server.
Free to start. No card required.
AI agents call emem_state 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_state 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_state": {}
}
}See the full emem — Earth memory protocol policy for all 81 tools.
These attack patterns abuse exactly the kind of access emem_state 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.
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. When to use: Call this when the user wants a machine-usable summary of a place rather than individual band readings — e.g. 'give me a feature vector for this location', 'how do I represent this place for ML', or before running similarity / linear-probe / clustering downstream. Also use it to get one rebindable handle (memory_token / state_cid) that cites the whole place. Default view=encoder is the cheap single-recall path; pass view=cube for the full attested view (its coverage[] lets you tell signed-zero from not-yet-materialised). Then hand the vector to emem_find_similar (k-NN), emem_compare (two-place cosine), or emem_verify_receipt (audit the signature).. 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_state: 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_state 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_state 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_state. 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_state 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.
Free to start. No card required.
4,600+ MCP servers and 31,000+ tools scanned and risk-classified.