detect_temporal_signal
Analyze observations in a stream and detect temporal signals: momentum (sustained directional trend), regime_shift (rolling mean shift), anomaly (z-score outlier), causal_hint, opportunity_window, or risk_window. Returns detected signals with confidence scores.
This record as markdown: /tools/io-github-homenshum-nodebench/detect-temporal-signal.md
What detect_temporal_signal does on Nodebench
AI agents call detect_temporal_signal to retrieve information from Nodebench without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
Why detect_temporal_signal is rated Low
This is a data analysis tool that reads input stream data and computes statistical/analytical results (momentum, regime shifts, anomalies via z-score). It performs inference on observations but does not create, modify, delete, or execute any actions—it only returns structured analytical findings. The most severe category applicable is Read, as it retrieves/infers insights from data without side effects.
From the tool's definition Tool analyzes observations and detects signals, returning results with confidence scores. The description uses analytical verbs (detect, analyze) with no mention of modification, deletion, or execution of external code. The output is signal detection metadata.
Attacks that exploit this kind of access
The rule that runs detect_temporal_signal 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 detect_temporal_signal, this is the rule to start with:
detect_temporal_signal is read-only, so it stays allowed. 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 detect_temporal_signal call is checked against it from then on.
Questions about detect_temporal_signal
Analyze observations in a stream and detect temporal signals: momentum (sustained directional trend), regime_shift (rolling mean shift), anomaly (z-score outlier), causal_hint, opportunity_window, or risk_window. Returns detected signals with confidence scores. It is categorised as a Read tool in the Nodebench MCP Server, which means it retrieves data without modifying state.
Register the Nodebench MCP server in PolicyLayer and add a rule for detect_temporal_signal: 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.
detect_temporal_signal 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 detect_temporal_signal 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 detect_temporal_signal. 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.
detect_temporal_signal 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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