Run a simplified Dingo evaluation based on a high-level goal. Automatically infers the appropriate evaluation type and settings based on the evaluation goal description. Args: input_path: Path to the file to evaluate. evaluation_goal: Description of what to evaluate (e.g., 'check for inappropriat...
AI agents invoke run_quick_evaluation to trigger actions in Dingo MCP Server. 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.
This tool executes evaluation logic with arguments that determine behavior (evaluation_goal, input_path). While it does not modify files or execute arbitrary code, it triggers external operations whose outcome is argument-dependent, placing it in Execute rather than Read.
From the tool's definition Tool runs an evaluation (triggering external operations), automatically infers settings based on input, and processes files at a given path.
Documented attack patterns abuse exactly the kind of access run_quick_evaluation gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Dingo MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for run_quick_evaluation:
{
"version": "1",
"default": "deny",
"tools": {
"run_quick_evaluation": {
"limits": [
{
"counter": "run_quick_evaluation_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}run_quick_evaluation 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.
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Run a simplified Dingo evaluation based on a high-level goal. Automatically infers the appropriate evaluation type and settings based on the evaluation goal description. Args: input_path: Path to the file to evaluate. evaluation_goal: Description of what to evaluate (e.g., 'check for inappropriate content', 'evaluate text quality', 'assess helpfulness'). Returns: A summary of the evaluation results or a path to the detailed results. It is categorised as a Execute tool in the Dingo MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Dingo MCP Server MCP server in PolicyLayer and add a rule for run_quick_evaluation: 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 Dingo MCP Server. Nothing to install.
run_quick_evaluation 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 run_quick_evaluation 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 run_quick_evaluation. 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.
run_quick_evaluation is provided by the Dingo MCP Server MCP server (migoxlab/dingo). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Dingo MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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6 Dingo MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.