Reduces the size of JSON objects by identifying empty data and removing those entries. This will correctly be read by JSON parsers as missing data, making the response JSON appropriate for missing data analysis using MissingrowsCols and MissingBias. LLMs should use this when handling any JSON tha...
Risk signalsAccepts raw HTML/template content (payload)
Part of the Scientific Microservices server.
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AI agents call sanitize_dataset to retrieve information from Scientific Microservices without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.
Even though sanitize_dataset only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.
Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.
{
"version": "1",
"default": "deny",
"tools": {
"sanitize_dataset": {}
}
}See the full Scientific Microservices policy for all 5 tools.
These attack patterns abuse exactly the kind of access sanitize_dataset gives an agent. Each links to the full case and the policy that stops it:
Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.
Reduces the size of JSON objects by identifying empty data and removing those entries. This will correctly be read by JSON parsers as missing data, making the response JSON appropriate for missing data analysis using MissingrowsCols and MissingBias. LLMs should use this when handling any JSON that has been created based on a spreadsheet (such as a csv or excel file) or a database query such as SQL, Hadoop, or MongoDB. Example Input: {"payload": [{"Category":"","Price":4436,"Rating":4.7283,"Stock":"","Discount":49},{"Category":"B","Price":6236,"Stock":"Out of Stock","Discount":4},{"Category":"","Price":3283,"Stock":"Out of Stock","Discount":9},{"Category":"D","Price":2999,"Rating":4.426,"Stock":"","Discount":40},{"Category":"","Rating":2.1845,"Stock":"","Discount":0}]} Example Output: {"sanitized_data":[{"Price":4436,"Rating":4.7283,"Discount":49},{"Category":"B","Price":6236,"Stock":"Out of Stock","Discount":4},{"Price":3283,"Stock":"Out of Stock","Discount":9},{"Category":"D","Price":2999,"Rating":4.426,"Discount":40},{"Rating":2.1845,"Discount":0}]}. It is categorised as a Read tool in the Scientific Microservices MCP Server, which means it retrieves data without modifying state.
Register the Scientific Microservices MCP server in PolicyLayer and add a rule for sanitize_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 Scientific Microservices. Nothing to install.
sanitize_dataset 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 sanitize_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 sanitize_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.
sanitize_dataset is provided by the Scientific Microservices MCP server (https://mcp.scientificmicroservices.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 5 Scientific Microservices tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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