Record a prediction for performance tracking. Agents can log their predictions and later check accuracy via get_performance. Records: which market, predicted outcome (YES/NO), predicted probability, and confidence level. When the market resolves, your Brier score and calibration are computed auto...
Part of the Telekash server.
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AI agents invoke track_prediction to trigger processes or run actions in Telekash. Execute operations can have side effects beyond the immediate call -- triggering builds, sending notifications, or starting workflows. Rate limits and argument validation are essential to prevent runaway execution.
track_prediction can trigger processes with real-world consequences. An uncontrolled agent might start dozens of builds, send mass notifications, or kick off expensive compute jobs. PolicyLayer enforces rate limits and validates arguments to keep execution within safe bounds.
Execute tools trigger processes. Rate-limit and validate arguments to prevent unintended side effects.
{
"version": "1",
"default": "deny",
"tools": {
"track_prediction": {
"limits": [
{
"counter": "track_prediction_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}See the full Telekash policy for all 29 tools.
These attack patterns abuse exactly the kind of access track_prediction gives an agent. Each links to the full case and the policy that stops it:
Other execute tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Record a prediction for performance tracking. Agents can log their predictions and later check accuracy via get_performance. Records: which market, predicted outcome (YES/NO), predicted probability, and confidence level. When the market resolves, your Brier score and calibration are computed automatically. Use this to build a track record. Agents with verified accuracy get higher trust scores.. It is categorised as a Execute tool in the Telekash MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Telekash MCP server in PolicyLayer and add a rule for track_prediction: 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 Telekash. Nothing to install.
track_prediction is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the track_prediction 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 track_prediction. 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.
track_prediction is provided by the Telekash MCP server (telekash-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 29 Telekash tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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