jp-m2m-gateway

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

With no annotations provided, the description carries full burden. It discloses the paid nature, HTTP 402 response, and beta limitations. However, it does not describe rate limits, authentication requirements, or the format of the successful response.

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 concise with three sentences, each adding new information: data source, payment mechanism, and beta note. No unnecessary words. It is front-loaded with the core purpose.

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 description covers the tool's purpose, payment, and beta status, but lacks details on the response format or data structure, especially since no output schema is provided. It adequately handles the simple parameter but leaves some 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 only parameter, corporate_id, is described in the schema with format and example. Schema coverage is 100%. The description adds no additional semantic context beyond what the schema provides, meeting the baseline.

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 title and description explicitly state the tool returns raw text blocks of statutory financial disclosures from the Official Gazette. It clearly distinguishes from sibling tools like get_government_records and search_corporations by specifying the source and data type.

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 payment mechanism (0.05 USDC per call, HTTP 402) and private beta conditions, but does not provide guidance on when to use this tool versus alternatives. No explicit when-to-use or when-not-to-use advice is given.

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. It mentions 'FREE during beta' and 'returns live data', which implies read-only, but does not explicitly state idempotency, rate limits, or authentication requirements. The note about attribution is helpful.

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?

Three sentences conveying essential information without redundancy. Could be more structured with bullet points, but effective as is.

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 simplicity of the tool (one parameter, no output schema), the description adequately covers the return data type, source, and attribution. Lacks details on output format, but context is sufficient for basic usage.

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% with a single parameter. The description does not add any information beyond the schema's '13-digit Japanese corporate number' documentation, so it meets the baseline.

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 it returns live, un-aggregated procurement contracts, subsidy grants, and certifications from gBizINFO. It distinguishes itself from sibling tools by specifying its unique data source and type.

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 like `get_gazette_financials` or `search_corporations`. The description does not 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?

Discloses key behaviors: paid per call (0.05 USDC), returns 402 challenge, private beta with receipt and eventual dataset access. No annotations to contradict.

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?

Three sentences each adding value: purpose, payment/challenge behavior, beta context. Slightly verbose 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?

Adequate for a paid tool with one parameter. Explains atypical behavior (402 challenge) and beta limitations. No output schema needed given return type described.

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 parameter fully with 100% description coverage. Description adds minimal extra context (e.g., example format) but does not significantly enhance meaning.

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 clearly states it returns un-aggregated month-over-month insured headcount deltas for a Japanese corporate number, with specific resource details. Distinguishes from siblings by focusing on pension data.

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?

Explains payment via x402 and that it returns HTTP 402 challenge, but does not explicitly state when to use or provide alternatives compared to sibling tools.

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 covers basic behavior: it is a catalog lookup with per-call pricing. However, it does not disclose output format, pagination, or potential errors. The 'free' vs 'pricing' inconsistency harms 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 brief at three sentences, with no filler. The first sentence is short, but the contradiction between 'free' and 'pricing' could be resolved. Otherwise, it is 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 one parameter and no output schema, the description covers the core purpose and pricing but lacks details on the structure of the dataset list, whether results are paginated, or any limitations. It is adequate but not thorough.

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 describes the corporate_id parameter. The tool description adds nothing beyond repeating the schema description (e.g., 'Japanese corporate number'), so it provides no additional parameter semantics.

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 lists raw regulatory datasets for a Japanese corporate number, which distinguishes it from siblings like get_gazette_financials. However, the phrase 'Free catalog lookup' conflicts with the mention of per-call pricing, causing slight confusion about whether the lookup itself is free.

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 explicit guidance on when to use this tool versus siblings or alternatives. The suggestion to 'Use the dataset tools or the returned api_url to retrieve data' is a post-usage hint, not a decision rule for tool selection.

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. It discloses that the tool is free and returns live data. However, it does not mention other behaviors like partial matching, result format, or limitations such as rate limiting or Japanese-only input.

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, front-loading the key value proposition ('FREE — returns live data') and then explaining the core purpose. Every sentence is essential and concise.

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 search tool with two parameters and no output schema, the description adequately explains the output (corporate numbers) and follow-up use. It lacks mention of pagination or result structure, but is largely 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?

Schema coverage is 100%, and the description adds no additional parameter-specific meaning beyond what the schema already provides (name with partial match, page with default 1). Baseline applies.

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 resolves Japanese company names to 13-digit corporate numbers via gBizINFO, a specific verb+resource. The sibling tools are all different types of records (financials, government, pension, datasets), so this tool is easily distinguished.

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 mentions using the corporate number with dataset tools, implying the context of use. However, it does not explicitly contrast with sibling tools or state when not to use this tool, but the sibling names make the distinction clear.

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