Run AI quality check on all locales of a specific translation key in SANDBOX and PERSIST the results to sandbox. Results are saved to sandbox (score, level, comment) and visible in Admin UI. Uses the default locale sandbox value as source text for comparison. Skips locales marked as 'expected' (m...
AI agents use check_entry_quality to create or update resources in Localization — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Localization environment.
| Parameter | Type | Required | Description |
|---|---|---|---|
key | string | Yes | Translation key to check |
namespace | string | Yes | Namespace slug |
projectSlug | string | Yes | Project slug |
Parameters from the server's own tool schema.
The tool runs a quality check and persistently writes the results (score, level, comment) to the sandbox environment, making them visible in the Admin UI. This is a Write operation — it creates/modifies data (quality check results) reversibly in sandbox. It does not touch production, limiting blast radius, hence medium severity.
From the tool's definition PERSIST the results to sandbox... Results are saved to sandbox (score, level, comment) and visible in Admin UI
Risk signalsAdmin/system-level operation
Attacks that exploit this kind of access
Run AI quality check on all locales of a specific translation key in SANDBOX and PERSIST the results to sandbox. Results are saved to sandbox (score, level, comment) and visible in Admin UI. Uses the default locale sandbox value as source text for comparison. Skips locales marked as 'expected' (manually accepted). Production is never read or written by this tool. It is categorised as a Write tool in the Localization MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
check_entry_quality accepts 3 parameters: key, namespace, projectSlug. Required: key, namespace, projectSlug. The full parameter table on this page comes from the server's own tool schema.
Register the Localization MCP server in PolicyLayer and add a rule for check_entry_quality: 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 Localization. Nothing to install.
check_entry_quality 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 check_entry_quality 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 check_entry_quality. 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.
check_entry_quality is provided by the Localization MCP server (localization-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
Teams ship this data inside their own products. See what a licence covers →