nodebench.research_role
Run or reuse public role research, store public hiring or market claims, and return a compact role dossier.
This record as markdown: /tools/io-github-homenshum-nodebench/nodebench.research-role.md
What nodebench.research_role does on Nodebench
AI agents invoke nodebench.research_role 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 nodebench.research_role is rated High
The tool performs active execution of research workflows (not merely reading) and stores results, which makes it Execute rather than Read or Write alone. The ability to 'run' research means it triggers external operations whose effects depend on arguments provided. The 'store' component adds persistence but is secondary to the execution aspect.
From the tool's definition 'Run or reuse public role research' and 'store public hiring or market claims' indicate the tool executes research operations and persists data to storage based on user input.
Attacks that exploit this kind of access
The rule that runs nodebench.research_role 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 nodebench.research_role, this is the rule to start with:
nodebench.research_role 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 nodebench.research_role call is checked against it from then on.
Questions about nodebench.research_role
Run or reuse public role research, store public hiring or market claims, and return a compact role dossier. 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 nodebench.research_role: 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.
nodebench.research_role 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 nodebench.research_role 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 nodebench.research_role. 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.
nodebench.research_role 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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