log_recon_finding
Record a finding from reconnaissance research. Link it to a recon session and categorize it. Use for both external discoveries (SDK changes, blog posts) and internal findings (codebase patterns, existing implementations).
This record as markdown: /tools/io-github-homenshum-nodebench/log-recon-finding.md
What log_recon_finding does on Nodebench
AI agents use log_recon_finding 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 log_recon_finding is rated Medium
This tool writes/creates data by recording and storing reconnaissance findings linked to a session. It does not execute code, delete data, or move money. Misuse could pollute research records with false findings, but effects are reversible (records can be deleted or corrected), making Write the appropriate category at medium severity.
From the tool's definition 'Record a finding from reconnaissance research' and 'Link it to a recon session and categorize it' — creates/stores a new finding record
Attacks that exploit this kind of access
The rule that runs log_recon_finding 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 log_recon_finding, this is the rule to start with:
log_recon_finding 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 log_recon_finding call is checked against it from then on.
Questions about log_recon_finding
Record a finding from reconnaissance research. Link it to a recon session and categorize it. Use for both external discoveries (SDK changes, blog posts) and internal findings (codebase patterns, existing implementations). 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 log_recon_finding: 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.
log_recon_finding 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 log_recon_finding 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 log_recon_finding. 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.
log_recon_finding 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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