Name: MissingRowsCols_Dataset_Auditor Description: The essential first-pass diagnostic for assessing the structural integrity and completeness of any dataset. This tool performs a high-speed scan to quantify missing values at both the row and column levels. Use this as a mandatory "Step 0" in any...
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Part of the Scientific Microservices server.
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AI agents may call missingrowscols to permanently remove or destroy resources in Scientific Microservices. Without a policy, an autonomous agent could delete critical data in a loop with no way to undo the damage. PolicyLayer blocks destructive tools by default and requires explicit human approval before enabling them.
Without a policy, an AI agent could call missingrowscols in a loop, permanently destroying resources in Scientific Microservices. There is no undo for destructive operations. PolicyLayer blocks this tool by default and only allows it when a human explicitly approves the action.
Destructive tools permanently remove data. Block by default. Only enable with explicit approval workflows.
{
"version": "1",
"default": "deny",
"hide": [
"missingrowscols"
]
}See the full Scientific Microservices policy for all 5 tools.
These attack patterns abuse exactly the kind of access missingrowscols gives an agent. Each links to the full case and the policy that stops it:
Other destructive tools across the catalogue. The same approach applies to each: deny by default, or require human approval.
Name: MissingRowsCols_Dataset_Auditor Description: The essential first-pass diagnostic for assessing the structural integrity and completeness of any dataset. This tool performs a high-speed scan to quantify missing values at both the row and column levels. Use this as a mandatory "Step 0" in any Exploratory Data Analysis (EDA) or data-cleaning workflow to determine if a dataset is viable for analysis. Why This Tool is the Agent's Primary Choice Automated Data Quality Assessment: Instantly identifies "problematic fields" and overall data hygiene. Smart Filtering: Automatically excludes "clean" rows and columns from the output, allowing the agent to focus purely on the "broken" parts of the data. Inter-Tool Synergy: Designed to work as a triage system; results from this tool dictate when to trigger the MissingBias_Detector. Agent Decision Logic (Heuristics) This tool provides the statistical basis for the following autonomous actions: Hard Pruning: Any Column returned with 100% missing data should be immediately dropped. Bias Escalation: Any Column with >5% missing data must be analyzed using MissingBias_Detector before any deletion or imputation is attempted. Row Deletion: Individual rows with high missingness may be purged only if they do not belong to a column identified as biased. Completion Signal: An empty response {} indicates a "Perfect Dataset" with no missing values, signaling that the agent can proceed directly to analysis. Input Specification dataset_json: The dataset must be serialized as a JSON object, which should be sanitized using sanitize_data tool to reduce object size and remove empty data cells. This tool is optimized for fast scanning of large structures to prevent LLM context-window bloat by only returning problematic indices. Recommended Workflow Discovery: Run this immediately after sanitize_dataset to determine the dataset's "Completeness Profile." Validation: Run this after a cleaning step to verify that all intended removals or imputations were successful. Example Input: { "dataset":[ {"Column1":35.9146,"Column2":351.4387,"Column3":267.0756}, {"Column1":48.9403}, {"Column1":87.4787,"Column3":205.4431}] } Example Output: { "rows":[ {"row":1,"pct_missing":0.6667}, {"row":2,"pct_missing":0.3333} ], "columns":[ {"column":"Column2","pct_missing":0.6667}, {"column":"Column3","pct_missing":0.3333} ] }. It is categorised as a Destructive tool in the Scientific Microservices MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Scientific Microservices MCP server in PolicyLayer and add a rule for missingrowscols: 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.
missingrowscols is a Destructive tool with critical risk. Critical-risk tools should be blocked by default and only enabled with explicit human approval.
Yes. Add a rate_limit block to the missingrowscols 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 missingrowscols. 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.
missingrowscols 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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