close_sprint
Close a sprint and generate four audience-specific close-out reports (marketing, sales, technical, executive) from its tickets, work log, and metrics (velocity, tokens, $ cost, ADRs touched, CRM ticket links).
This record as markdown: /tools/ai-featureboard-featureboard/close-sprint.md
What close_sprint does on FeatureBoard
AI agents use close_sprint to create or update resources in FeatureBoard, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your FeatureBoard environment.
Why close_sprint is rated Medium
Closing a sprint is a Write operation: it transitions a sprint to a closed state and generates derivative reports (marketing, sales, technical, executive summaries). This is reversible—sprints can typically be reopened in project boards. It modifies project state but does not irreversibly delete data (not Destructive), does not execute arbitrary code (not Execute), and does not move money (not Financial).
From the tool's definition close_sprint closes a sprint (modifies state) and generates reports, but does not delete or destroy data. The description states it generates four report outputs from existing sprint data—a reversible state change typical of project management workflow…
Attacks that exploit this kind of access
The rule that runs close_sprint safely
PolicyLayer is an MCP gateway: it sits between your AI agents and FeatureBoard, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For close_sprint, this is the rule to start with:
close_sprint 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 FeatureBoard, apply this rule, and every close_sprint call is checked against it from then on.
Questions about close_sprint
Close a sprint and generate four audience-specific close-out reports (marketing, sales, technical, executive) from its tickets, work log, and metrics (velocity, tokens, $ cost, ADRs touched, CRM ticket links). It is categorised as a Write tool in the FeatureBoard MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the FeatureBoard MCP server in PolicyLayer and add a rule for close_sprint: 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 FeatureBoard. Nothing to install.
close_sprint 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 close_sprint 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 close_sprint. 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.
close_sprint is provided by the FeatureBoard MCP server (https://github.com/valentil/featureboard-mcp/releases/download/v0.7/featureboard.plugin). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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