uratori（ウラトリ）

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

With no annotations, the description fully describes the tool's output: definitions, methods, and limitations. It is a simple read-only tool with no side effects or auth requirements mentioned, but the description is transparent enough for the task.

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-loaded with the main output, and every sentence adds value. 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?

Given no parameters and no output schema, the description covers the return content (definitions, methods, limitations) and usage recommendation. It is complete 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?

The tool has no parameters, so per guidelines the baseline is 4. The description adds no parameter info beyond what the schema provides, which is sufficient.

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 returns definitions of 5 judgment categories and 4 subcategories of 'not_found', along with investigation methods and limitations. It distinguishes from the sibling tool by recommending reading this before using research_vendor_certifications.

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 explicitly recommends reading this before interpreting results from research_vendor_certifications, providing clear context for use. It lacks explicit when-not-to-use guidance, but the recommendation is strong and informative.

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, and it handles this well. It discloses that results come from real research of public sources, that '未確認' is not a claim of non-certification, that results are reference-only and not a safety/certification guarantee, and that the operation may take tens of seconds. This gives the agent appropriate expectations about reliability, latency, and interpretation of outputs.

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 dense but every clause earns its place: purpose, source, classification scheme, evidence output, caveats, and operational notes. It is front-loaded with the core purpose before adding limitations and performance expectations, and avoids unnecessary repetition.

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?

There is no output schema, so the description must explain what the agent can expect back, and it does: five judgment categories, evidence URLs, and acquisition dates. It also covers edge interpretation ('未確認' is not denial), the reference-only nature of results, and the option to reduce ambiguity via corporate number. This is sufficient for a look-up/research tool of this complexity.

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 100%, so the baseline is 3 even without additional parameter explanations. The description does reinforce the value of corporateNumber for disambiguation and mentions serviceUrl for improved matching, but those details already appear in the schema. It adds little beyond what the schema already communicates.

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 verb ('実調査して返す'), resource (a single Japanese vendor's third-party certifications/audit reports), and scope (public registries and official sites). It lists concrete certification types and explicitly says it covers one vendor, which distinguishes it from a general-purpose or batch lookup tool. The five-tier judgment system further sharpens what the tool returns.

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 clear context for when to use the tool: researching certification status for one Japanese vendor using public information. It also explains that specifying a corporate number avoids misidentifying similar-named companies and notes the tool is free but may take tens of seconds. However, it does not explicitly discuss when not to use it or mention the sibling tool as an alternative.

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