report_feedback

SERVERDataNexus MCP SOURCEdev-7bd0/mcp-server

Medium RISK CLASS

Category Write

Parameters 80 required

Recommended Rate-limitedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/dev-7bd0-mcp-server/report-feedback.md

WHAT IT DOES

What report_feedback does on DataNexus MCP

AI agents use report_feedback to create or update resources in DataNexus MCP, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your DataNexus MCP environment.

Parameter	Type	Required	Description
`signal`	string	—	One of incorrect_data, missing_field, stale_data, not_useful, wrong_entity, or data_quality. Required for user_feedback.
`comment`	string	—	Description of the issue. Optional. Max 500 characters.
`tool_id`	string	—	Tool identifier, e.g. T04 or security_fetch_cve_detail. Required.
`query_hash`	string	—	Hash from the response being reported — found in the query_hash field of any response. Required.
`feedback_type`	string	—	user_feedback (default) or agent_gap. Use agent_gap when the tool returned a valid response but did not serve the user's actual need.
`intended_query`	object	—	What the agent was trying to accomplish — used when feedback_type=agent_gap. Optional. Max 256 chars.
`missing_fields`	object	—	List of field names that are absent or wrong. Optional.
`gap_description`	object	—	What was missing or wrong in the result — used when feedback_type=agent_gap. Optional. Max 256 chars.

Parameters from the server's own tool schema.

RISK

Why report_feedback is rated Medium

This tool submits structured feedback data to the DataNexus platform, creating a new record or issue report. It has side effects (a feedback entry is created/logged) but is reversible in nature (no deletion or financial action). It fits Write as it posts/creates data on the server side. Misuse could spam or pollute the feedback system, hence medium severity.

From the tool's definition Report a data quality issue or agent intent gap for a DataNexus tool response — submits feedback with fields like signal, comment, missing_fields, feedback_type, intended_query, gap_description

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs report_feedback 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 report_feedback, this is the rule to start with:

report_feedback Rate-limited

report_feedback stays usable, but capped: an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "report_feedback": {
      "limits": [
        {
          "counter": "report_feedback_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

LIMIT THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect DataNexus MCP, apply this rule, and every report_feedback call is checked against it from then on.

FAQ

Questions about report_feedback

What does the report_feedback tool do? +

Report a data quality issue or agent intent gap for a DataNexus tool response. tool_id: e.g. "T10" or "security_fetch_cve_detail". query_hash: From the query_hash field of the response. signal: incorrect_data | missing_field | stale_data | not_useful | wrong_entity | data_quality. comment: Issue description. Max 500 chars. missing_fields: Absent or wrong field names. feedback_type: "user_feedback" (default) or "agent_gap". intended_query: Agent's goal. Max 256 chars. gap_description: What was missing. Max 256 chars. Example: report_feedback(tool_id="T10", query_hash="abc123", signal="incorrect_data"). It is categorised as a Write tool in the DataNexus MCP MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

What parameters does report_feedback accept? +

report_feedback accepts 8 parameters: signal, comment, tool_id, query_hash, feedback_type, intended_query, missing_fields, gap_description. The full parameter table on this page comes from the server's own tool schema.

How do I enforce a policy on report_feedback? +

Register the DataNexus MCP server in PolicyLayer and add a rule for report_feedback: 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.

What risk level is report_feedback? +

report_feedback is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit report_feedback? +

Yes. Add a rate_limit block to the report_feedback 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.

How do I block report_feedback completely? +

Set action: deny in the PolicyLayer policy for report_feedback. 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.

What MCP server provides report_feedback? +

report_feedback 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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