Get the place's fingerprint from several AI models at once (geotessera, clay_v1, prithvi_eo2, galileo) in one call, returned as a per-model map. Each model is tried independently; any that can't produce a vector here show up under missing with a reason instead of failing the whole request. When t...
Part of the emem — Earth memory protocol server.
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AI agents use emem_state_multi to create or modify resources in emem — Earth memory protocol. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.
Without a policy, an AI agent could call emem_state_multi repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach emem — Earth memory protocol.
Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.
{
"version": "1",
"default": "deny",
"tools": {
"emem_state_multi": {
"limits": [
{
"counter": "emem_state_multi_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}See the full emem — Earth memory protocol policy for all 81 tools.
These attack patterns abuse exactly the kind of access emem_state_multi gives an agent. Each links to the full case and the policy that stops it:
Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Get the place's fingerprint from several AI models at once (geotessera, clay_v1, prithvi_eo2, galileo) in one call, returned as a per-model map. Each model is tried independently; any that can't produce a vector here show up under missing with a reason instead of failing the whole request. When to use: Call this when the user wants a second (or third) opinion on what a place looks like — 'do the different models agree this is forest / urban / water?', 'which model has the freshest read here?', or when you want all the embeddings concatenated for a stronger downstream classifier. Use the single-model emem_state instead when one embedding is enough. Pass encoders: [...] to narrow the set.. It is categorised as a Write tool in the emem — Earth memory protocol MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the emem — Earth memory protocol MCP server in PolicyLayer and add a rule for emem_state_multi: 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_multi is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the emem_state_multi 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_multi. 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_multi 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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