ingest_temporal_observation
Ingest a raw observation into the temporal substrate (timeSeriesObservations). Supports numeric, categorical, event, and text observations from any source type. Returns observation ID for linking to signals and causal chains.
This record as markdown: /tools/io-github-homenshum-nodebench/ingest-temporal-observation.md
What ingest_temporal_observation does on Nodebench
AI agents use ingest_temporal_observation to create or update resources in Nodebench, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Nodebench environment.
Why ingest_temporal_observation is rated Medium
The tool writes new data (observations) into a time-series store. It creates records reversibly and returns an ID, indicating a standard write/create operation with no indication of irreversible deletion or financial consequences. Misuse could pollute a time-series dataset with bad data, hence medium severity.
From the tool's definition Ingest a raw observation into the temporal substrate (timeSeriesObservations)... Returns observation ID for linking to signals and causal chains.
Attacks that exploit this kind of access
The rule that runs ingest_temporal_observation safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Nodebench, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For ingest_temporal_observation, this is the rule to start with:
ingest_temporal_observation stays usable, but capped: an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Nodebench, apply this rule, and every ingest_temporal_observation call is checked against it from then on.
Questions about ingest_temporal_observation
Ingest a raw observation into the temporal substrate (timeSeriesObservations). Supports numeric, categorical, event, and text observations from any source type. Returns observation ID for linking to signals and causal chains. It is categorised as a Write tool in the Nodebench MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Nodebench MCP server in PolicyLayer and add a rule for ingest_temporal_observation: 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 Nodebench. Nothing to install.
ingest_temporal_observation 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 ingest_temporal_observation 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 ingest_temporal_observation. 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.
ingest_temporal_observation is provided by the Nodebench MCP server (nodebench-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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