JSONShelf

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

No annotations are provided, so the description carries full burden. It reveals the 'soft-cast' behavior with an example, but lacks details on mutation, error handling, or the exact meaning of 'soft' (e.g., whether non-coercible values are left unchanged or cause failure).

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?

Single sentence with an example, efficiently communicating the core action without redundancy.

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?

No output schema, no annotations, and only a one-sentence description. The tool's return value, mutation behavior, and edge cases are unaddressed, making it incomplete for an agent to invoke reliably.

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?

Both parameters are undocumented in the schema (coverage 0%). The description references 'values' and 'schema' but does not clarify what 'input' should be (e.g., a JSON object, array, or scalar). The example hints at coercion of individual values but is insufficient for understanding the expected structure.

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 clearly identifies the tool's function: soft-casting values to match a schema. The example (string '42' → 42 for integer fields) provides concrete illustration. It distinguishes from siblings like validate (checking) and repair (fixing schema).

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 implies usage when you need to coerce values to a schema but does not explicitly contrast with alternatives like validate or repair. No guidance on when not to use it.

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 were provided, so the description carries the full burden of behavioral disclosure. It states the tool generates a 'minimal valid example' – implying it produces a simple, schema-conforming output – but does not disclose potential side effects, return format, or behavior on invalid schemas. Given the lack of annotations, this is insufficient for safety and expectation-setting.

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 a single, front-loaded sentence with no waste. It immediately states the action and object, achieving maximum clarity in minimal length.

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?

The tool is simple (one parameter, no output schema), but the description omits information about the returned example's format or any limitations. While not overly complex, the lack of annotations and output schema means the description should provide a bit more context about what constitutes 'minimal valid' and what the caller should expect. It is adequate but has clear gaps.

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 0%, so the description must compensate for the single 'schema' parameter. The phrase 'matching a JSON schema' clarifies that the parameter is a JSON schema and implies it is used to generate the example, which adds meaning beyond the bare 'object' type in the input schema. However, it does not explain expected structure or constraints on the schema, leaving room for ambiguity.

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 'Generate a minimal valid example matching a JSON schema' uses a specific verb ('Generate') and resource ('a minimal valid example matching a JSON schema'), clearly distinguishing it from sibling tools like validate, coerce, and repair. The purpose is unambiguous.

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 provides clear context that this tool is for generating an example from a schema, but does not explicitly state when to use it over alternatives or mention exclusions. Since the sibling tools have different purposes, the intended use is clear, but the description could be improved by explicitly noting it is for creating examples, not validating or transforming data.

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 full burden of behavioral disclosure. It reveals the return format (valid JSON plus a diff) but does not explain the mode parameter effects, side effects, error behavior, or how 'repair' differs from 'coerce'. This leaves significant behavioral ambiguity.

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 a single sentence, entirely free of filler or redundancy. It front-loads the primary purpose and return type. However, it is so brief that it sacrifices valuable context, making it less effective than a slightly longer but more informative description.

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?

Despite the lack of an output schema, the description does state what is returned (valid JSON plus diff). However, with no annotations, no explanation of the two modes, no differentiation from the sibling tool, and no guidance on the optional schema, the description is insufficient for an agent to confidently invoke this tool in complex scenarios.

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 67%, with 'input' and 'schema' having descriptions but 'mode' only having an enum. The description adds no parameter details at all, failing to explain the semantics of 'strict' vs 'coerce' or how the optional schema is used. It could compensate for the missing coverage but does not.

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 clearly states the action ('Repair') and the resource ('malformed JSON'), and it adds a distinctive return value (diff of fixes). However, it does not explicitly differentiate from the sibling jsonshelf_coerce, which likely performs a similar function, so it loses a point for missing sibling distinction.

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 provides no guidance on when to use this tool versus alternatives like jsonshelf_coerce or jsonshelf_validate. It does not mention prerequisites, exclusions, or suitable contexts. This is a clear gap.

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 must disclose behavior. It mentions 'Returns structured errors with JSON Pointer paths and remedies,' which partially explains behavior but omits the success case (what is returned when validation passes), whether it throws, or side effects. This is a moderate level of transparency.

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 short sentences with no redundant words. It is front-loaded with the core purpose, and the second sentence adds valuable detail about the return format. Highly concise and well-structured.

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?

The tool has two required parameters, no annotations, and no output schema, so the description alone must explain behavior fully. It leaves out what happens on successful validation, how invalid JSON input (versus schema failure) is handled, and what 'remedies' specifically entail. This is a significant completeness gap for a validation tool.

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 0%, so the description must explain the parameters. It implies 'input' is JSON and 'schema' is a schema, but does not explicitly describe their format, types, or nuances (e.g., what constitutes valid JSON, schema draft version). The description adds minimal meaning beyond the parameter names.

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 clearly states the tool validates JSON against a schema with the specific verb 'Validate' and identifies its output: structured errors with JSON Pointer paths and remedies. This distinguishes it from sibling tools like jsonshelf_coerce and jsonshelf_repair, which imply different operations.

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 use this tool versus alternatives. It doesn't mention conditions like 'use when you need to check data against a schema' or contrast with coercion/repair. There is no explicit when/when-not or alternative selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.