dive_interaction_test
Define and track a structured interaction test for a component. Provide preconditions and a sequence of test steps (action, target, expected outcome). The agent executes each step via the MCP Bridge (browser_click, browser_type, etc.), takes screenshots, and records actual results here. Each step...
This record as markdown: /tools/io-github-homenshum-nodebench/dive-interaction-test.md
What dive_interaction_test does on Nodebench
AI agents invoke dive_interaction_test to trigger actions in Nodebench. 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 dive_interaction_test is rated High
This tool drives real browser interactions (clicks, typing) through an MCP Bridge, executing arbitrary UI actions against components. It spans Read (screenshots) and Execute (browser actions), so Execute is the most severe applicable category. Misuse could trigger unintended UI operations, form submissions, or state changes in live systems.
From the tool's definition The agent executes each step via the MCP Bridge (browser_click, browser_type, etc.), takes screenshots, and records actual results
Attacks that exploit this kind of access
The rule that runs dive_interaction_test 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 dive_interaction_test, this is the rule to start with:
dive_interaction_test 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 Nodebench, apply this rule, and every dive_interaction_test call is checked against it from then on.
Questions about dive_interaction_test
Define and track a structured interaction test for a component. Provide preconditions and a sequence of test steps (action, target, expected outcome). The agent executes each step via the MCP Bridge (browser_click, browser_type, etc.), takes screenshots, and records actual results here. Each step gets pass/fail status. The test aggregates into an overall result. This creates the detailed walkthrough with preconditions, steps, expected vs actual, and visual evidence at each step. It is categorised as a Execute tool in the Nodebench MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Nodebench MCP server in PolicyLayer and add a rule for dive_interaction_test: 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.
dive_interaction_test 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 dive_interaction_test 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 dive_interaction_test. 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.
dive_interaction_test 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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