Normalize entity names, domains, URLs and emails into stable machine-readable canonical values. One successful call consumes 1 HumanMirror Forge credit.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Entity name string or object with fields such as name, domain, url and email. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It does disclose a real side effect (one successful call consumes 1 Forge credit), which is useful and not inferable from the schema. However, it does not address mutability/destructiveness, output shape, or any rate limits, 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 short sentences, no filler; the core purpose is front-loaded and the credit cost is a distinct, useful second sentence.
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 single-parameter tool with no annotations and no output schema, this is serviceable but not complete: it omits any statement about the return value, error behavior, or batching, and gives no examples of canonicalization. The credit-cost note is a plus, but an agent still has to guess at edge-case input handling.
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% – the schema already explains `input` as a string or object with entity fields. The description reiterates the entity types without adding format, acceptable key names, or output examples, so value over schema is minimal.
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?
States a specific action ('Normalize') and the objects it applies to (entity names, domains, URLs, emails), plus the intended result (stable machine-readable canonical values). This makes its role obvious next to siblings like clean_json or score_data_quality, which solve different problems.
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?
Provides no guidance on when to choose this over siblings such as dedupe_records or clean_json, and no exclusions or prerequisites. The only implied signal is the action itself, so an agent gets no routing help.
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.