extract

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

No annotations are provided, so the description must carry the full burden. It does not disclose behavioral traits such as read-only nature, authentication needs, rate limits, or idempotency. The term 'machine-readable' is insufficient to cover behavioral expectations.

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, concise sentence with no superfluous words. It is well-structured and front-loaded, stating the action and resource immediately.

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 parameterless tool lacking annotations and output schema, the description provides the basic purpose but omits details about the return value format or additional behavior. While adequate, it could be more complete by specifying the expected output (e.g., JSON or XML).

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 no parameters, providing 100% coverage. The description adds minor value with 'machine-readable', implying the output format, but no parameter details are needed since there are none.

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 'Get' and the resource 'Mart402 product/pricing catalog', making the tool's purpose unambiguous. It distinguishes from siblings like extract_url or parse_document, which handle different 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?

No guidance on when to use this tool versus alternatives. The description only mentions 'machine-readable', which hints at format but does not specify contexts, 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?

No annotations are provided, so the description carries full burden. It mentions cost and output format but does not disclose whether the operation is read-only, destructive, or has rate limits, which are important for an agent to choose safely.

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 very concise with a single sentence and cost note. It front-loads key information but could benefit from more structured details about the output format.

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 tool with one parameter and no output schema, the description covers the basic purpose and cost. However, it lacks detail on output structure, error handling, and input validation, leaving gaps for an agent.

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 has 0% property description coverage, and the description only implies the 'url' parameter should be a Japanese company website. No format, constraints, or examples are given, so minimal added 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 clearly states the tool converts a Japanese company website into a standardized JSON profile with specific fields (name, address, etc.). This indicates a specific verb-resource relationship. However, it does not differentiate from sibling tools like structured_extract or parse_document, which could also extract structured data from websites.

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?

No guidance is given on when to use this tool versus alternatives, nor are there any prerequisites or exclusion criteria. The description lacks context on input requirements beyond 'Japanese company site'.

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?

Discloses billing model (free quote, pay-per-page), auto-routing for document type, and OCR strength for Japanese. No annotations provided, so description carries burden well.

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 covering purpose and key behavioral traits. No wasted words.

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?

Adequate for a single-parameter tool with no output schema. Covers purpose, pricing, routing, and OCR. Lacks details on output structure or error cases.

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?

Only parameter 'url' has no schema description. Description adds no parameter-specific details beyond tool purpose.

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?

Clearly states 'PDF to structured Markdown', a specific verb and resource. Distinct from sibling tools like extract_url or structured_extract.

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?

Implied use for PDF conversion, but no explicit guidance on when to use vs alternatives like structured_extract. Mentions pricing model but no 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?

Without annotations, the description discloses the hybrid LLM+deterministic method, benchmark performance (F1 1.000), and cost ($0.02/call via x402). It does not mention side effects or idempotency, but for a read-like extraction this is reasonable. No contradictions.

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 sentence packs the core purpose, method, benchmark, and cost with no wasted words. Ideal for quick comprehension.

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 2 parameters and no output schema, the description omits details like parameter formats, error behavior, and return structure. The benchmark and cost are useful, but completeness is average for the tool's simplicity.

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%, and the description does not explain the url or schema parameters. It mentions 'Fill YOUR JSON schema' but gives no details on schema format, constraints, or how url is used. This is insufficient for correct invocation.

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 fills a JSON schema from a URL using a hybrid approach. The verb 'fill' is specific but slightly informal, and the benchmark details add credibility. However, it could be clearer that it extracts structured data according to the provided 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?

No guidance is given on when to use this tool versus siblings like extract_url or parse_document. The description does not mention context, prerequisites, or exclusions, leaving the agent to infer usage.

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, so description carries full burden. Mentions specific checks and cost, but lacks details on side effects, authentication, error behavior, or whether it is read-only. Pricing info adds some value.

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 concise sentences: first defines purpose and specifics, second adds pricing. No superfluous words, directly 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?

Despite reasonable purpose clarity, the description lacks parameter details, output format specifics, error handling, and prerequisites. For a tool with 2 nested parameters and no output schema, this is insufficient for an agent to use 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%, and description does not explain the two parameters ('fields', 'line_items') beyond their names. No guidance on expected structure or content, leaving the agent without context to populate them correctly.

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?

Description states specific verb 'verify' and resource 'invoice fields', listing concrete checks (tax math, line-item sum, Japan check-digit) and output format (accounting-ready JSON). Clearly distinguishes from sibling tools like extract_url or parse_document.

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?

Implies usage after invoice extraction ('extracted invoice fields') but does not explicitly state when to use versus alternatives, nor provide prerequisites or exclusions.

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