Perform comprehensive quality control on a BIDS dataset. This tool runs multiple quality checks on a dataset to ensure it meets BIDS standards and best practices. Useful for pre-upload validation before sharing data or uploading to databases. Args: paths: List of all file paths in the dataset (re...
Risk signalsBulk/mass operation — affects multiple targets · Admin/system-level operation
Part of the Neuro server.
Free to start. No card required.
AI agents use quality_control_dataset to create or modify resources in Neuro. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.
Without a policy, an AI agent could call quality_control_dataset repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach Neuro.
Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.
{
"version": "1",
"default": "deny",
"tools": {
"quality_control_dataset": {
"limits": [
{
"counter": "quality_control_dataset_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}See the full Neuro policy for all 34 tools.
These attack patterns abuse exactly the kind of access quality_control_dataset gives an agent. Each links to the full case and the policy that stops it:
Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Perform comprehensive quality control on a BIDS dataset. This tool runs multiple quality checks on a dataset to ensure it meets BIDS standards and best practices. Useful for pre-upload validation before sharing data or uploading to databases. Args: paths: List of all file paths in the dataset (relative to BIDS dataset root, starting with '/') Returns: Comprehensive quality control report with checks and recommendations Examples: - quality_control_dataset(['/dataset_description.json', '/participants.tsv', '/sub-01/anat/sub-01_T1w.nii.gz']). It is categorised as a Write tool in the Neuro MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Neuro MCP server in PolicyLayer and add a rule for quality_control_dataset: 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 Neuro. Nothing to install.
quality_control_dataset 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 quality_control_dataset 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 quality_control_dataset. 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.
quality_control_dataset is provided by the Neuro MCP server (@brain-bbqs/neuro-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 34 Neuro tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
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