deeptrace_create_evidence_pack
Bundle multiple evidence chunks into a named evidence pack. Evidence packs are the unit of provenance — they capture what an agent saw at investigation time.
This record as markdown: /tools/io-github-homenshum-nodebench/deeptrace-create-evidence-pack.md
What deeptrace_create_evidence_pack does on Nodebench
AI agents use deeptrace_create_evidence_pack 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 deeptrace_create_evidence_pack is rated Medium
This tool creates and structures data (evidence bundles) within what appears to be an investigation/provenance system. While it organizes and persists information, it does not execute external code, trigger irreversible deletions, or move funds. The primary action is data creation/organization, fitting the Write category.
From the tool's definition Tool description states it 'Bundle[s] multiple evidence chunks into a named evidence pack' — a create/write operation that produces a new named artifact ('evidence pack'). The operation is reversible in nature (packs can be modified, deleted, or recreated).
Attacks that exploit this kind of access
The rule that runs deeptrace_create_evidence_pack 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 deeptrace_create_evidence_pack, this is the rule to start with:
deeptrace_create_evidence_pack 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 deeptrace_create_evidence_pack call is checked against it from then on.
Questions about deeptrace_create_evidence_pack
Bundle multiple evidence chunks into a named evidence pack. Evidence packs are the unit of provenance — they capture what an agent saw at investigation time. 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 deeptrace_create_evidence_pack: 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.
deeptrace_create_evidence_pack 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 deeptrace_create_evidence_pack 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 deeptrace_create_evidence_pack. 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.
deeptrace_create_evidence_pack 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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