compute_calibration
Compute calibration bins and Brier aggregates across all resolved forecasts. Returns 10 bins (0-10%, 10-20%, ..., 90-100%) with predicted vs observed frequency.
This record as markdown: /tools/io-github-homenshum-nodebench/compute-calibration.md
What compute_calibration does on Nodebench
AI agents call compute_calibration 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 compute_calibration is rated Low
This tool aggregates and analyzes existing forecast data to produce statistical calibration metrics. It performs read-only operations on historical forecast data—computing summary statistics and frequency distributions. There is no creation, modification, deletion, or execution of external code involved. The output is purely informational for quality assessment purposes.
From the tool's definition Tool description states it 'Compute[s] calibration bins and Brier aggregates across all resolved forecasts' and 'Returns 10 bins...with predicted vs observed frequency.' The verbs 'compute' and 'returns' indicate data retrieval and analysis with no…
Attacks that exploit this kind of access
The rule that runs compute_calibration 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 compute_calibration, this is the rule to start with:
compute_calibration 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 compute_calibration call is checked against it from then on.
Questions about compute_calibration
Compute calibration bins and Brier aggregates across all resolved forecasts. Returns 10 bins (0-10%, 10-20%, ..., 90-100%) with predicted vs observed frequency. 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 compute_calibration: 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.
compute_calibration 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 compute_calibration 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 compute_calibration. 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.
compute_calibration 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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