get_gate_preset
Get the rules for a built-in quality gate preset. Returns rule names, descriptions, and evaluation instructions so you can check each one. Presets: engagement (content quality), code_review (implementation quality), deploy_readiness (pre-deploy checklist), ui_ux_qa (frontend UI/UX verification), ...
This record as markdown: /tools/io-github-homenshum-nodebench/get-gate-preset.md
What get_gate_preset does on Nodebench
AI agents call get_gate_preset to retrieve information from Nodebench without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
Why get_gate_preset is rated Low
This tool retrieves and returns existing quality gate preset definitions. It performs a read-only query operation that returns rule metadata and descriptions without modifying, executing, or deleting any data. The action is informational only, making it a Read category tool with low severity since misuse would only expose preset configuration information.
From the tool's definition Tool description states it 'Get[s] the rules for a built-in quality gate preset. Returns rule names, descriptions, and evaluation instructions' — purely retrieval of preset configuration data with no side effects.
Attacks that exploit this kind of access
The rule that runs get_gate_preset safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Nodebench, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For get_gate_preset, this is the rule to start with:
get_gate_preset 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 Nodebench, apply this rule, and every get_gate_preset call is checked against it from then on.
Questions about get_gate_preset
Get the rules for a built-in quality gate preset. Returns rule names, descriptions, and evaluation instructions so you can check each one. Presets: engagement (content quality), code_review (implementation quality), deploy_readiness (pre-deploy checklist), ui_ux_qa (frontend UI/UX verification), agent_bug_verdict (evidence-first QA verdict discipline), agent_comparison (A/B agent eval), a11y (WCAG 2.1 AA accessibility audit), visual_regression (baseline visual diff checks). It is categorised as a Read tool in the Nodebench MCP Server, which means it retrieves data without modifying state.
Register the Nodebench MCP server in PolicyLayer and add a rule for get_gate_preset: 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 Nodebench. Nothing to install.
get_gate_preset 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 get_gate_preset 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 get_gate_preset. 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.
get_gate_preset is provided by the Nodebench MCP server (nodebench-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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