merge_research_results
Merge parallel sub-agent research results into a unified dataset. Takes arrays of records from multiple sources (e.g., Hotel Discovery Agent, Price Agent, Crowd Agent), aligns them by a join key, detects conflicts, and persists the merged dataset. Designed for coordinator agents aggregating paral...
This record as markdown: /tools/io-github-homenshum-nodebench/merge-research-results.md
What merge_research_results does on Nodebench
AI agents use merge_research_results to create or update resources in Nodebench, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Nodebench environment.
Why merge_research_results is rated Medium
This tool creates or modifies data (merge and persist operations) without permanently deleting or overwriting irreversible data. While it aggregates information from multiple sources and handles conflicts, the core function is data consolidation and storage—a Write operation.
From the tool's definition Tool description explicitly states it 'persists the merged dataset' and 'takes arrays of records from multiple sources' to 'merge' and align them by join key. The action of persisting merged data is a reversible write/modification operation.
Attacks that exploit this kind of access
The rule that runs merge_research_results 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 merge_research_results, this is the rule to start with:
merge_research_results 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.
The button opens the PolicyLayer dashboard: create your workspace, connect Nodebench, apply this rule, and every merge_research_results call is checked against it from then on.
Questions about merge_research_results
Merge parallel sub-agent research results into a unified dataset. Takes arrays of records from multiple sources (e.g., Hotel Discovery Agent, Price Agent, Crowd Agent), aligns them by a join key, detects conflicts, and persists the merged dataset. Designed for coordinator agents aggregating parallel research. It is categorised as a Write tool in the Nodebench MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Nodebench MCP server in PolicyLayer and add a rule for merge_research_results: 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.
merge_research_results 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 merge_research_results 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 merge_research_results. 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.
merge_research_results 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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