graduate_project
One-command incubator \u2192 dedicated-repo graduation (lifecycle \
This record as markdown: /tools/ai-featureboard-featureboard/graduate-project.md
What graduate_project does on FeatureBoard
AI agents invoke graduate_project to trigger actions in FeatureBoard. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.
Why graduate_project is rated High
This tool executes a complex, multi-step operation that transitions a project from incubator status to a dedicated repository. While it likely involves write operations (creating a repo, moving data), the primary concern is that it triggers an automated external operation (repository creation/graduation pipeline) whose full effects depend on arguments.
From the tool's definition 'One-command incubator → dedicated-repo graduation (lifecycle' - triggers a multi-step automated lifecycle transition that creates a dedicated repository and graduates a project
Attacks that exploit this kind of access
The rule that runs graduate_project 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 graduate_project, this is the rule to start with:
graduate_project stays usable, but rate-capped: a runaway agent can't fire it dozens of times 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 graduate_project call is checked against it from then on.
Questions about graduate_project
One-command incubator \u2192 dedicated-repo graduation (lifecycle \. It is categorised as a Execute tool in the FeatureBoard MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the FeatureBoard MCP server in PolicyLayer and add a rule for graduate_project: 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.
graduate_project 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 graduate_project 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 graduate_project. 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.
graduate_project 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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