JSONShelf

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

With no annotations provided, the description carries full burden but only states the core behavior of type coercion. It lacks details on error handling (e.g., what happens if coercion fails), performance implications, or side effects. The term 'soft-cast' is somewhat vague, leaving behavioral traits like strictness or fallback mechanisms unclear.

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, efficient sentence that front-loads the key action ('soft-cast values to match a schema') and includes a clarifying example. There is zero waste, making it appropriately sized for a simple tool.

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 complexity (2 parameters with 0% schema coverage, no output schema, and no annotations), the description is incomplete. It doesn't explain the input and schema parameters adequately, lacks details on return values or error cases, and doesn't address how it differs from sibling tools in practice, leaving gaps for an AI agent to use it correctly.

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 but only vaguely references 'input' and 'schema' without explaining their roles, formats, or constraints. It mentions examples like string '42' for integer fields but doesn't clarify if 'input' is the value or data structure, or what 'schema' should contain. This adds minimal meaning beyond the bare 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 clearly states the verb 'soft-cast' and the resource 'values to match a schema', with specific examples like converting string '42' to integer 42. It distinguishes from siblings by focusing on type coercion rather than validation, repair, or example generation, though it doesn't explicitly name those alternatives.

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 type mismatches need gentle conversion (e.g., string to integer), suggesting it's for handling loosely typed input. However, it doesn't explicitly state when to use this vs. siblings like jsonshelf_validate or jsonshelf_repair, nor does it mention prerequisites or exclusions.

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 full burden of behavioral disclosure. It states the tool generates examples but lacks details on traits like output format, determinism, error handling, or performance constraints. This is a significant gap for a tool with no annotation coverage.

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, efficient sentence that front-loads the core purpose without unnecessary words. Every part of the sentence earns its place by specifying the action, resource, and target.

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 tool's complexity (generating examples from JSON schemas), lack of annotations, no output schema, and low parameter coverage, the description is incomplete. It doesn't address key aspects like what constitutes a 'minimal valid example', how examples are structured, or potential limitations, making it inadequate for informed use.

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 input schema has 1 parameter with 0% description coverage, and the tool description adds no information about the 'schema' parameter's meaning, format, or constraints. This fails to compensate for the low schema coverage, leaving the parameter semantics entirely undocumented.

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's purpose with a specific verb ('generate') and resource ('minimal valid example'), and identifies the target ('matching a JSON schema'). It doesn't explicitly differentiate from sibling tools like jsonshelf_coerce, jsonshelf_repair, or jsonshelf_validate, but the focus on example generation is distinct enough to avoid confusion.

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 its siblings (coerce, repair, validate). It implies usage for generating examples from schemas but offers no context on alternatives, exclusions, or prerequisites, leaving the agent to infer usage scenarios.

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 full burden of behavioral disclosure. It effectively describes the tool's behavior: it repairs malformed JSON and returns valid JSON plus a diff of fixes. This covers the core functionality and output, though it lacks details on error handling, performance, or limitations.

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 front-loaded with the core purpose in the first sentence and adds output details in the second. Both sentences earn their place by providing essential information without redundancy, making it highly efficient 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?

Given moderate complexity (3 parameters, no output schema, no annotations), the description is somewhat complete but has gaps. It explains what the tool does and returns, but lacks details on parameter usage, error cases, or how it interacts with sibling tools, leaving room for improvement in context.

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 one parameter ('input') well-described and another ('schema') partially described, while 'mode' lacks description. The description does not add meaning beyond the schema, as it mentions no parameters. Baseline is 3 since schema coverage is moderate, but the description does not compensate for gaps.

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 specific action ('Repair malformed JSON') and the resource being acted upon (JSON data). It distinguishes from sibling tools by focusing on repair rather than coercion, validation, or example generation, making the purpose unambiguous and distinct.

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 dealing with malformed JSON, but it does not explicitly state when to use this tool versus alternatives like 'jsonshelf_coerce' or 'jsonshelf_validate'. The context is clear (repairing invalid JSON), but no exclusions or direct comparisons to siblings are provided.

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 full burden. It discloses that the tool returns structured errors with JSON Pointer paths and remedies, which adds some behavioral context beyond the basic validation action. However, it lacks details on error handling, performance, or any limitations, leaving significant gaps for a tool with no annotation coverage.

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, efficient sentence that front-loads the core purpose and includes key behavioral details (structured errors, JSON Pointer paths, remedies). Every word earns its place with no redundancy or unnecessary elaboration.

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 no annotations, 0% schema coverage, and no output schema, the description is incomplete. It covers the basic purpose and error format but lacks details on parameter usage, error types, or integration with sibling tools. For a validation tool with complex inputs, more context is needed to guide effective use.

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. It implies that 'input' is the JSON to validate and 'schema' is the schema to validate against, but does not specify formats, constraints, or examples. With 2 required parameters and no schema details, the description adds minimal semantic value beyond what can be inferred from the tool name.

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 specific action ('Validate JSON against a schema') and the resource involved (JSON and schema). It distinguishes from sibling tools like 'jsonshelf_coerce', 'jsonshelf_example', and 'jsonshelf_repair' by focusing on validation rather than coercion, example generation, or repair.

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. It does not mention prerequisites, such as needing a valid JSON schema, or compare it to sibling tools like 'jsonshelf_repair' for handling invalid JSON. Usage is implied only by the tool's name and purpose.

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