AI agents use update_result 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 modifies existing test run results rather than creating new ones, which is reversible and does not permanently delete data. The blast radius is medium because incorrect updates could corrupt test records or mislead test reporting, affecting project quality assurance workflows. It is not Destructive (data can be reverted), not Execute (no code execution or external triggers), and not Financial.
From the tool's definition Tool name 'update_result' and description 'Update an existing test run result' indicate modification of existing data in the Qase test management platform.
Documented attack patterns abuse exactly the kind of access update_result 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_result:
{
"version": "1",
"default": "deny",
"tools": {
"update_result": {
"limits": [
{
"counter": "update_result_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}update_result 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 run result. 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_result: 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_result 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_result 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_result. 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_result 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.
Free to start. No card required.
26 QASE MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.