Remove duplicate JSON records using exact canonical matching or selected key fields. One successful call consumes 1 HumanMirror Forge credit.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Array of JSON records. | |
| options | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosing behavior. It usefully mentions that one successful call consumes 1 HumanMirror Forge credit, which is important context. However, it does not disclose whether the output is a new array, whether ordering is preserved, or how invalid input is handled.
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 two sentences with no filler. The core operation is front-loaded, and the credit consumption warning is appended concisely. Every sentence adds value.
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?
Given there is no output schema, the description does not explicitly state what the tool returns (e.g., deduplicated array). It also omits details about edge cases like empty input or duplicate key field handling. The credit-cost disclosure is valuable, but the overall guidance is not fully complete for an agent encountering this tool for the first time.
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?
The schema covers only the 'input' parameter (50% coverage). The description compensates by explaining that matching can use 'selected key fields', which clarifies the purpose of the undocumented 'options.fields' property. It also implies the default behavior (exact canonical matching) when fields are not provided.
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 action ('Remove duplicate JSON records') and specifies two matching methods ('exact canonical matching or selected key fields'). It clearly identifies the resource (JSON records) and the operation, making it easy for an agent to distinguish this from siblings like normalize_entity or clean_json.
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 siblings such as clean_json or detect_anomaly. It states what the tool does, but not the conditions under which it should be preferred or avoided.
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.