DataNexus MCP

report_feedback

Read-only

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")

Input Schema

TableJSON Schema

Name	Required	Description	Default
`signal`	Yes	One of incorrect_data, missing_field, stale_data, not_useful, wrong_entity, or data_quality. Required for user_feedback.
`comment`	No	Description of the issue. Optional. Max 500 characters.
`tool_id`	Yes	Tool identifier, e.g. T04 or security_fetch_cve_detail. Required.
`query_hash`	Yes	Hash from the response being reported — found in the query_hash field of any response. Required.
`feedback_type`	No	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.	user_feedback
`intended_query`	No	What the agent was trying to accomplish — used when feedback_type=agent_gap. Optional. Max 256 chars.
`missing_fields`	No	List of field names that are absent or wrong. Optional.
`gap_description`	No	What was missing or wrong in the result — used when feedback_type=agent_gap. Optional. Max 256 chars.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.3/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description indicates a write operation ('Report'), but annotations set readOnlyHint=true, suggesting no side effects. This is a contradiction as submitting feedback likely involves data storage. The description does not clarify this inconsistency or disclose the actual behavioral impact.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is verbose but well-structured with clear sections and examples. It front-loads the purpose and then details each parameter. A slightly more compact presentation could improve readability, but it remains effective.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the availability of a detailed input schema and output schema (inferred from context), the description fully complements these with usage context, parameter details, and constraints. It leaves no significant gaps for an agent to misinterpret.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema coverage, the baseline is 3, but the description adds significant value by explaining parameter usage, providing examples, stating character limits, and clarifying when to use optional fields like intended_query and gap_description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: reporting data quality issues or agent intent gaps for DataNexus tool responses. It uses a specific verb ('Report') and identifies the resource ('data quality issue or agent intent gap'), distinguishing it from sibling tools that primarily fetch data or generate API keys.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool, including when to set feedback_type to 'agent_gap' versus 'user_feedback'. It also includes an example of a typical call, helping the agent understand usage context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.1/5.0

Disambiguation4/5

Tools are grouped into clear domain prefixes (compliance, domain, frontend_security, etc.) with distinct purposes. Minor overlap exists between frontend_security_detect_typosquatting and security_detect_typosquatting, but descriptions clarify the different scope.

Naming Consistency4/5

Most tools follow a consistent verb_noun pattern with snake_case. Irregularities like 'fetch' vs 'audit' and two 'detect_typosquatting' tools exist, but overall naming is predictable within domains.

Tool Count3/5

55 tools is high for a single server given the breadth of domains. Some redundancy (e.g., two typosquatting tools) suggests possible trimming, but the count is justified by the wide coverage.

Completeness4/5

The tool surface covers key operations across domains like compliance, domain, security, legal, and nonprofit. Minor gaps exist, such as limited frontend audit beyond package.json and no general-purpose code scanning.

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