jsonaut

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

No annotations provided, so description carries full burden. It discloses free deterministic path, paid LLM fallback with payment details, and optional schema shaping. Missing details on rate limits or output format, but overall transparent about core behavior.

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?

Four sentences, front-loaded with main purpose, then free/paid distinction, then schema advice. Each sentence earns its place. Could be more compact but no 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?

Moderate complexity (3 params, no output schema). Description covers extraction modes, payment, and schema use. Lacks explicit output description but reasonably complete for its class.

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% for all 3 parameters. The description adds context beyond schema: explains schema role 'shape the output' and allow_llm_fallback's payment implications. This adds value, justifying above baseline 3.

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 extracts JSON from arbitrary text, with repair and validation. It distinguishes from siblings (infer_schema, repair_json, validate_json) by combining extraction with optional LLM fallback and schema shaping, making it the primary extraction tool.

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 explains when to use deterministic extraction vs paid LLM fallback, including the x402 payment requirement and success-only charging. It also suggests using a JSON Schema to shape output. Implicitly, it is for messy text, but explicit when-not-to-use guidance is missing.

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 provided, but description covers key behaviors: free, deterministic, and explains how as_samples merges multiple samples. Lacks details on output format or errors, but adequate for the tool's simplicity.

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 sentences, no redundancy. First sentence defines core purpose, second sentence adds crucial parameter guidance and a real-world use case. Efficient and impactful.

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 output schema and simple parameters, the description fully covers what the agent needs to know to use the tool correctly, including the key as_samples nuance.

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 covers both parameters with descriptions. The description adds value by explaining when to set as_samples=true, which is not obvious from the schema alone.

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 infers a JSON Schema from an example value, using specific verb and resource. It distinguishes from siblings like extract_json and repair_json by noting it is free, deterministic, and turns sample output into reusable 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?

Provides explicit guidance for using the as_samples parameter when input is an array of same-shape objects. Does not directly compare with alternatives, but the context is clear and helpful for the intended use case.

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 full burden. It discloses key behaviors: deterministic free repair, paid LLM fallback with payment requirement (x402, charged on success), and the ability to validate against a schema. This is transparent, though it could mention that the output is a repaired JSON string.

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 sentences, no fluff. The first sentence front-loads the main purpose with specific examples. Every part is informative.

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 (repair, validation, two-tier), the description covers the main aspects. It lacks an output description but the purpose is clear. Siblings are mentioned but not differentiated explicitly. Still adequate.

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%, but the description adds value by explaining the two-tier repair for allow_llm_fallback and the schema validation role. This goes beyond the schema's basic descriptions.

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: repairing malformed JSON with specific examples (trailing commas, single quotes, truncation, etc.). It also mentions optional schema validation/coercion. This distinguishes it from siblings like extract_json or validate_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 explains the two-tier repair process (free deterministic vs paid LLM) but does not explicitly tell when to use this tool over siblings. While it's implied for malformed JSON, there is no direct comparison or exclusion criteria.

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 full burden. It discloses return of validity and violations, but does not mention safety, side effects, or error behavior. Adequate but not detailed.

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 sentences with no wasted words. First sentence states purpose, second adds return value and cost. Front-loaded and efficient.

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 output schema, the description mentions return format (validity and violations). Sibling tools are distinct. Could specify format details, but acceptable for a simple 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 coverage is 100%, and the description does not add meaning beyond the existing parameter descriptions. Baseline 3 applies as 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 clearly states the verb 'validate' and the resources 'JSON string' and 'JSON Schema'. It distinguishes from siblings like extract_json or repair_json by specifying validation as the core function.

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 use for validation versus extraction or repair, but does not explicitly state when to use or not use this tool. However, sibling tool names provide context.

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