Data Quality Gate - deterministic post-scrape cleaner + verdict

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

No annotations are provided, so the description carries the full burden. It discloses important behavioral traits: 100% determinism, no LLM, freeness of this endpoint, and the limitation that datasetId is not resolvable. It also lists the exact output facts and conditional cross-source price divergence, providing a detailed behavioral model.

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

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

The description is front-loaded with a clear call-to-action and efficiently lists output facts. The cost comparison with the POST /api endpoint adds useful context but also extra length. Overall, it is well-organized but could be slightly tightened without losing essential information.

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

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

The description covers the tool's purpose, input format, output details, determinism, and the datasetId limitation. However, it does not explicitly state whether rawJson is required (the schema lists 0 required parameters), nor does it describe error behavior or what happens when both parameters are absent. Given no output schema, these are minor gaps in an otherwise complete description.

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

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

Schema description coverage is 100%, so the baseline is 3. The tool description largely repeats the schema's parameter details (rawJson as JSON array or object; datasetId not resolvable) without adding new syntax, validation, or usage nuances. The only addition is a slight emphasis on rawJson, but it does not go beyond what the schema already states.

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

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

The description clearly states the tool checks dataset quality and returns a deterministic verdict (RELIABLE / USABLE_WITH_CLEANING / UNRELIABLE) with specific quality facts. It distinguishes itself from the alternative paid POST /api endpoint, making the purpose and scope unambiguous.

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

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

Explicitly instructs to call before using any dataset, and provides clear alternatives: the paid POST /api endpoint and the recommendation to pass rawJson instead of datasetId. This gives strong contextual guidance for when and how to use the tool.

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

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

With no annotations, the description carries the full burden and excels: it discloses the response shape (no data key), the deterministic/no-LLM nature, the explicit list of repairs (HTML tags, mojibake, invisible chars), and the boundary of what is repaired vs. reported rather than silently changed. It also reveals cost/authentication requirements for the paid endpoint, which is valuable behavioral context beyond the schema.

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

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

Though long, the description is densely informative and structured via headers and a prominent warning. Every section ('WHAT YOU GET HERE', 'HOW TO GET THE REPAIRED DATA', 'PROVENANCE', 'PAID CALL BEHAVIOR') earns its place, and the front-loaded warning prevents misuse. The structure aids scannability despite the length.

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

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

The tool has no output schema, but the description fully compensates: it explains the exact output structure, the fields returned, the calling flow for the paid service, the deterministic nature, and the repair/report boundary. It also preempts common mistakes (e.g., writing output back into the dataset) and provides a URL for the full rules. This is a complete picture for an agent to invoke correctly.

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

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

Schema description coverage is 100%, so the schema already documents both parameters (rawJson and options with detailed defaults/enums). The main description does not add parameter-level semantics beyond what the schema provides; it focuses on tool behavior. This meets the baseline 3 for high schema coverage, but does not exceed it.

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

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

The description explicitly states the tool does NOT return cleaned data, instead returning an inventory of defects and instructions for the paid repair endpoint. The verb 'returns' plus the precise resource ('exact inventory of the defects in YOUR data') makes the purpose unambiguous, and the contrast with the paid call distinguishes it from sibling tools.

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

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

The description clearly explains when to use this free endpoint (detection, no account needed) versus the paid call for repaired data, and explicitly names check_dataset_quality as reporting the same defects. It also provides a concrete alternative path ('make the one call named in get_the_repaired_data') and a rationale for the split.

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

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

With no annotations, the description fully discloses critical behavior: response has no 'data' key, returns a payment_required status, and should not be written back to the dataset. It explains what the free tier returns, how to obtain the repaired data, and the audit trail details, including reversibility via inverse patch.

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

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

The description is lengthy but well-structured with clear section headers (WHAT YOU GET HERE FREE, HOW TO GET THE REPAIRED DATA, etc.). The critical warning is front-loaded. Some redundancy exists ('READ THIS FIRST' repeated), but each section adds necessary information for a paid tool with complex behavior.

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

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

Despite no output schema, the description fully covers the response shape and important side effects, explains the payment and endpoint for the paid call, and provides a complete behavioral contract. Given the tool's complexity and the absence of annotations, the description leaves no critical questions unanswered.

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

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

Schema description coverage is 100%, and the schema already documents each parameter thoroughly with examples and caveats. The description does not add additional parameter-level semantics beyond what the schema provides, so baseline 3 is appropriate.

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

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

The description clearly states that this tool does NOT return cleaned data but instead returns an inventory of defects, including which repair rules would fire and which cases need decisions. It differentiates from sibling clean_scraped_data by emphasizing the audit/preview nature and the paid repair endpoint.

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

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

Explicitly provides when to use this tool: 'Use it when you must be able to PROVE later what changed and why.' It also contrasts with clean_scraped_data (same repair but without audit trail) and check_dataset_quality (same defects reported free). The 'WHY THE SPLIT' section gives clear context on the free vs paid distinction.

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