dcl_evaluate_batch
PRE-ACTION Bulk Processing ($0.10). Evaluates a list of items in one call; each item is a dict shaped {"response": str, "policy"?: str}, where policy defaults to "default" if omitted and may be any built-in policy name (default, strict, anti_jailbreak, safety, content_quality). Each item gets its...
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What dcl_evaluate_batch does on Dcl Trust Oracle
AI agents call dcl_evaluate_batch to retrieve information from Dcl Trust Oracle without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
items | array | Yes | List of items to evaluate, each shaped like {'response': str, 'policy'?: str}. |
agent_id | string | Yes | Identifier of the agent that produced the responses. |
Parameters from the server's own tool schema.
Why dcl_evaluate_batch is rated Low
Tool audits and assesses outputs against policies without altering data or executing external operations.
From the tool's definition Evaluates items; returns verdicts and results; no modification or execution.
Attacks that exploit this kind of access
The rule that runs dcl_evaluate_batch safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Dcl Trust Oracle, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For dcl_evaluate_batch, this is the rule to start with:
dcl_evaluate_batch 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 Dcl Trust Oracle, apply this rule, and every dcl_evaluate_batch call is checked against it from then on.
Questions about dcl_evaluate_batch
PRE-ACTION Bulk Processing ($0.10). Evaluates a list of items in one call; each item is a dict shaped {"response": str, "policy"?: str}, where policy defaults to "default" if omitted and may be any built-in policy name (default, strict, anti_jailbreak, safety, content_quality). Each item gets its own independent COMMIT/NO_COMMIT verdict via the same logic as the matching single-item evaluate_* tool; results are returned in input order under results, plus a shared batch_id. Capped at 200 items per call — oversized batches are rejected. Use this instead of multiple single-item evaluate_* calls when checking several responses — optionally against different policies — in one priced call rather than paying per item separately. It is categorised as a Read tool in the Dcl Trust Oracle MCP Server, which means it retrieves data without modifying state.
dcl_evaluate_batch accepts 2 parameters: items, agent_id. Required: items, agent_id. The full parameter table on this page comes from the server's own tool schema.
Register the Dcl Trust Oracle MCP server in PolicyLayer and add a rule for dcl_evaluate_batch: 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 Dcl Trust Oracle. Nothing to install.
dcl_evaluate_batch 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 dcl_evaluate_batch 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 dcl_evaluate_batch. 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.
dcl_evaluate_batch is provided by the Dcl Trust Oracle MCP server (https://mcp.fronesislabs.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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