get_self_eval_report
Generate a comprehensive self-evaluation report by cross-referencing all persisted data: verification cycles, eval runs, quality gates, gaps, learnings, recon sessions, and tool call trajectories. Identifies strengths, weaknesses, and areas for improvement.
This record as markdown: /tools/io-github-homenshum-nodebench/get-self-eval-report.md
What get_self_eval_report does on Nodebench
AI agents call get_self_eval_report to retrieve information from Nodebench without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
Why get_self_eval_report is rated Low
This tool reads and aggregates existing persisted data to produce an analytical report. It performs no write operations (data is not modified), executes no code or external commands, destroys no data, and involves no financial transactions. The sole function is retrieval and synthesis of stored information for reporting purposes, which is characteristic of Read category tools.
From the tool's definition Tool generates/retrieves a report by cross-referencing persisted data (verification cycles, eval runs, quality gates, gaps, learnings, recon sessions, tool call trajectories).
Attacks that exploit this kind of access
The rule that runs get_self_eval_report 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 get_self_eval_report, this is the rule to start with:
get_self_eval_report is read-only, so it stays allowed. 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 get_self_eval_report call is checked against it from then on.
Questions about get_self_eval_report
Generate a comprehensive self-evaluation report by cross-referencing all persisted data: verification cycles, eval runs, quality gates, gaps, learnings, recon sessions, and tool call trajectories. Identifies strengths, weaknesses, and areas for improvement. It is categorised as a Read tool in the Nodebench MCP Server, which means it retrieves data without modifying state.
Register the Nodebench MCP server in PolicyLayer and add a rule for get_self_eval_report: 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.
get_self_eval_report is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the get_self_eval_report 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 get_self_eval_report. 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.
get_self_eval_report 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.
More on Nodebench, and thousands of servers like it.
This server
Across the catalogue