validate_tool_output
Validate a DataNexus tool response for data quality issues using two-layer validation: deterministic rules first, then AI review for ambiguous cases. Read-only. Never blocks. tool_id: DataNexus tool identifier e.g. T04, T10, T22. Required. Find in the tool_id field of any response. query_hash: Ha...
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What validate_tool_output does on DataNexus MCP
AI agents call validate_tool_output to retrieve information from DataNexus MCP 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 |
|---|---|---|---|
tool_id | string | — | DataNexus tool identifier, e.g. T04, T10, T22 — found in the tool_id field of any response. Required. |
query_hash | string | — | Hash from the response being validated — found in the query_hash field of any response. Enables feedback correlation. Required. |
response_json | string | — | The full tool response, serialised as a JSON string, to validate for data quality issues. Required. |
Parameters from the server's own tool schema.
Why validate_tool_output is rated Low
This tool performs validation analysis on already-generated responses from other DataNexus tools. It retrieves and analyzes data quality information but does not modify, delete, execute commands, or trigger external operations. The input parameters (tool_id, query_hash, response_json) are metadata for validation context. The outputs (pass/issues_found) are pure read operations on response data.
From the tool's definition Tool description explicitly states 'Read-only. Never blocks.' It takes existing tool responses as input and validates them using deterministic rules and AI review, returning analysis without modifying or executing external operations.
Attacks that exploit this kind of access
The rule that runs validate_tool_output safely
PolicyLayer is an MCP gateway: it sits between your AI agents and DataNexus MCP, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For validate_tool_output, this is the rule to start with:
validate_tool_output 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 DataNexus MCP, apply this rule, and every validate_tool_output call is checked against it from then on.
Questions about validate_tool_output
Validate a DataNexus tool response for data quality issues using two-layer validation: deterministic rules first, then AI review for ambiguous cases. Read-only. Never blocks. tool_id: DataNexus tool identifier e.g. T04, T10, T22. Required. Find in the tool_id field of any response. query_hash: Hash from the response you are validating. Required. Enables feedback correlation. response_json: Full tool response serialised as a JSON string. Required. Returns pass or issues_found, with issues from each layer and whether feedback was auto-filed. Both layers must agree before feedback is filed. Use validate_tool_output to check data quality. Use report_feedback instead to manually report an issue you have already identified. If this tool's response does not serve the user's need, call report_feedback with feedback_type="agent_gap", tool_id="validate_tool_output", intended_query="{what the user needed}", gap_description="{what was missing or wrong in the result}". It is categorised as a Read tool in the DataNexus MCP MCP Server, which means it retrieves data without modifying state.
validate_tool_output accepts 3 parameters: tool_id, query_hash, response_json. The full parameter table on this page comes from the server's own tool schema.
Register the DataNexus MCP server in PolicyLayer and add a rule for validate_tool_output: 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 DataNexus MCP. Nothing to install.
validate_tool_output 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 validate_tool_output 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 validate_tool_output. 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.
validate_tool_output is provided by the DataNexus MCP server (dev-7bd0/mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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