Score record-array data quality using completeness, duplicate rate and field type consistency. One successful call consumes 1 HumanMirror Forge credit.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Non-empty array of JSON records. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden. It does provide one valuable non-obvious detail: one successful call consumes 1 HumanMirror Forge credit. However, it does not disclose what the return value looks like, whether data is modified or retained, or what happens on failure, so transparency is partial.
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?
Two compact sentences deliver the core purpose and the credit cost with no filler. The main action is front-loaded, and every sentence earns its place.
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?
With only one required parameter and no nested objects, the tool is simple to invoke, and the description covers input semantics and metering. However, since there is no output schema, the absence of any indication of what the tool returns or how scores are formatted leaves a moderate gap.
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 coverage is 100% and the only parameter is already described as a non-empty array of JSON records. The description reinforces that the input is a record array and ties it to the quality dimensions being scored, but it does not add substantive parameter-level meaning beyond the schema.
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 states a specific verb and resource: 'Score record-array data quality' using completeness, duplicate rate, and field type consistency. This clearly differentiates the tool from the sibling cleaning, deduplication, anomaly-detection, and normalization 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 gives no guidance on when to choose this tool over its siblings, such as dedupe_records or detect_anomaly. It does not state use cases, exclusions, or alternatives, leaving selection to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct data quality concern: JSON cleaning, record deduplication, numeric anomaly detection, entity normalization, and overall quality scoring. There is minimal overlap, and the descriptions clarify boundaries well.
All tool names follow a consistent verb_noun snake_case pattern (clean_json, dedupe_records, detect_anomaly, normalize_entity, score_data_quality). The naming is uniform and predictable, with only 'dedupe' being slightly informal but still clear.
Five tools is well-scoped for a data-quality-focused server. Each tool covers a meaningful operation without redundancy, and the count feels appropriate for the apparent purpose.
The surface covers the core data-quality lifecycle: cleaning, deduplication, anomaly detection, entity normalization, and scoring. Minor gaps exist such as explicit schema validation or date/number coercion, but the set is reasonably complete for common workflows.