compare_feature_forecasts
Replay and compare two owned saved delivery scenarios with the same cohort and window. Separately reports elapsed capture time, expected-date drift, remaining work, rate and exact modeled duration change. Date drift alone is not worsening throughput. Missing history or changed cohorts return nonc...
This record as markdown: /tools/com-meta-council-decision-intelligence/compare-feature-forecasts.md
What compare_feature_forecasts does on Meta Council
AI agents call compare_feature_forecasts to retrieve information from Meta Council without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
later_id | string | Yes | |
earlier_id | string | Yes |
Parameters from the server's own tool schema.
Why compare_feature_forecasts is rated Low
Even though compare_feature_forecasts only reads data, uncontrolled read access leaks sensitive information and racks up API costs: an agent caught in a retry loop can make thousands of calls a minute without anyone noticing.
Attacks that exploit this kind of access
The rule that runs compare_feature_forecasts safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Meta Council, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For compare_feature_forecasts, this is the rule to start with:
compare_feature_forecasts 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 Meta Council, apply this rule, and every compare_feature_forecasts call is checked against it from then on.
Questions about compare_feature_forecasts
Replay and compare two owned saved delivery scenarios with the same cohort and window. Separately reports elapsed capture time, expected-date drift, remaining work, rate and exact modeled duration change. Date drift alone is not worsening throughput. Missing history or changed cohorts return noncomparable; no attention reason or notification is created. It is categorised as a Read tool in the Meta Council MCP Server, which means it retrieves data without modifying state.
compare_feature_forecasts accepts 2 parameters: later_id, earlier_id. Required: later_id, earlier_id. The full parameter table on this page comes from the server's own tool schema.
Register the Meta Council MCP server in PolicyLayer and add a rule for compare_feature_forecasts: 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 Meta Council. Nothing to install.
compare_feature_forecasts 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 compare_feature_forecasts 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 compare_feature_forecasts. 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.
compare_feature_forecasts is provided by the Meta Council MCP server (https://meta-council.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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