Normalize JSON deterministically: trim strings, normalize object keys, optionally remove empty values, and sort keys for stable downstream processing. One successful call consumes 1 HumanMirror Forge credit.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Any JSON value to normalize. | |
| options | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full disclosure burden, and it does a good job: it reveals determinism, the normalization steps, optionality, and even the credit cost. It leaves some ambiguity around key-normalization rules and return format, but the core behavioral traits are disclosed.
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?
A single, information-dense sentence leads with the core purpose and front-loads the key operations, then closes with the credit cost. Every clause adds value and nothing is wasted.
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?
For a simple transform with no output schema and no annotations, the description is largely complete: an agent can infer inputs, options, and the resulting normalized JSON. It could be improved by detailing the key normalization rule and whether the input is mutated, but those are minor gaps for this tool's complexity.
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 only 50% and option properties lack descriptions, but the description maps each option to a behavior: trim_strings, normalize_keys, remove_empty, and sort_keys. It adds the meaning of 'optionally' but does not define the exact normalization convention for keys.
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 names a specific verb and resource: 'Normalize JSON deterministically' and lists concrete operations (trim strings, normalize object keys, remove empty values, sort keys). This clearly separates clean_json from siblings like dedupe_records or normalize_entity, which concern different data tasks.
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 phrase 'for stable downstream processing' gives a clear use context, but the description never states when not to use this tool or names alternatives such as normalize_entity. The usage guidance is implied rather than explicit.
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.