HumanMirror Forge

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.

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.

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

With no annotations provided, the description carries the burden of behavioral disclosure. It does well by stating the algorithm family (IQR, robust summary statistics), the return kind (machine-readable anomaly positions), and a concrete cost constraint ('One successful call consumes 1 HumanMirror Forge credit'). It does not cover failure modes or edge cases, but the core behavior is transparent.

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 method and output are front-loaded, and the credit-consumption note is relevant operational context. 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?

Given the simple single-parameter schema and no output schema, the description adequately covers what the tool does, how it does it, what it returns, and its cost. It could add explicit usage boundaries or edge-case behavior, but for this complexity level it is substantially complete.

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 already has 100% coverage for the single 'input' parameter, describing it as 'Numeric series.' The description mostly reaffirms that the tool processes numeric series without adding parameter-specific constraints, formats, or examples, so the schema does the heavy lifting.

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 uses a specific verb ('Detect') and resource ('numeric outliers') and adds the method ('using IQR and robust summary statistics') plus the output type ('machine-readable anomaly positions'). This clearly distinguishes it from siblings like clean_json or score_data_quality.

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 intended use is implied by 'Detect numeric outliers' and the numeric series parameter, but there is no explicit guidance about when to choose this tool over alternatives or when not to use it. The description names no sibling tools or exclusion conditions.

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?

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.

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.