AI agents use update_plan to create or update resources in QASE MCP Server — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your QASE MCP Server environment.
This tool creates or modifies data reversibly within the Qase test management platform. It updates a test plan's properties without deleting or destroying data. While it could potentially cause issues if an AI agent updates critical test plans with incorrect values, the modification is non-destructive and can be corrected. Confidence is high given the explicit mention of 'update' in both name and description.
From the tool's definition Tool name 'update_plan' and description 'Update an existing test plan' indicate modification of existing data. The action is reversible (plans can be further updated or reverted), distinguishing it from destructive operations.
Documented attack patterns abuse exactly the kind of access update_plan gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and QASE MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for update_plan:
{
"version": "1",
"default": "deny",
"tools": {
"update_plan": {
"limits": [
{
"counter": "update_plan_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}update_plan 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.
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Update an existing test plan. It is categorised as a Write tool in the QASE MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the QASE MCP Server MCP server in PolicyLayer and add a rule for update_plan: 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 QASE MCP Server. Nothing to install.
update_plan 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 update_plan 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 update_plan. 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.
update_plan is provided by the QASE MCP Server MCP server (rikuson/mcp-qase). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from QASE MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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26 QASE MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.