sukimalabo-tester

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

Annotations already declare readOnly/idempotent/non-destructive/closed-world, so the safety profile is covered. The description adds genuinely new behavioral context: the check never fetches the URL, cannot confirm the test exists or is open, and returns reasons and fixes in both Japanese and English.

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 purpose, scope, and limitations are front-loaded, and each sentence carries information. The bilingual duplication makes it long, but since returning bilingual output is a feature of the tool, the mirroring is defensible rather than wasteful.

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?

No output schema exists, yet the description explains the return content (verdict plus reason and fix, in JP and EN). Combined with the single documented parameter and the stated boundaries, an agent has everything needed to call it correctly and interpret the result.

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?

Single required parameter with 100% schema description coverage, so the schema already carries the meaning and gives an example. The description reinforces the canonical closed-test URL shape, but adds no format constraints beyond what the schema example implies. Baseline 3 is appropriate.

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?

States a specific verb and resource (format-check a Google Play testing opt-in URL without login) and enumerates the three classifications (closed test vs internal test vs store page) it distinguishes. An agent can tell exactly what this tool does and, via the explicit 'format check only' clause, what it does not.

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?

Gives two concrete trigger scenarios (verifying a URL to share with testers; diagnosing why the Play Console tester count didn't increase) plus an explicit exclusion: it does not open the URL and does not verify the test exists or is open. Siblings (get_service_info, how_to_get_testers) are distinct enough that no misrouting is likely.

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?

Annotations already declare readOnly/idempotent/non-destructive/non-open-world, so the safety profile is covered. The description adds real value beyond that: it discloses that numbers are read from operator settings at call time (a point-in-time snapshot that can change) and that it returns what the service does NOT promise, which sets expectations the annotations cannot.

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?

Front-loads the return summary and keeps the Google Play closed-test framing tight. The bilingual duplication is long but deliberate for a Japanese service, and each sentence carries content rather than filler.

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 informational tool with no output schema, the description enumerates the returned content (overview, mechanism, join flow, acceptance status, non-promises) and notes value volatility. An agent knows exactly what it will get without needing a schema.

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 takes zero parameters, so there is no parameter syntax to explain and the baseline is 4. Nothing in the description contradicts or obscures the empty 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?

States a specific resource (the Minna no Tester service) and enumerates exactly what it returns: overview, mechanism, joining flow, current listing-acceptance status, and explicit non-promises. It is easily distinguishable from siblings like list_recruiting_apps or check_optin_link, which cover different surface areas.

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?

Gives clear triggering conditions in both languages: use when the user asks what the service is, how to join/list, or whether a new app can be listed now. It stops short of naming alternative siblings or exclusions, but the intended context is unambiguous.

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?

Annotations already declare readOnlyHint, idempotentHint, non-destructive and openWorldHint=false, so the safety profile is covered. The description adds real behavioral context beyond that: the response includes cost estimates and cautions, is general-purpose rather than service-specific, and appends a short service note at the end. It still does not describe response structure or length.

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 content is front-loaded and every claim is substantive, but the Japanese and English text convey identical information, roughly doubling the length with no added meaning. Necessary for a bilingual audience, yet it costs conciseness.

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?

With no output schema, the description must convey what comes back, and it does: main methods, cost estimates, cautions, plus a service note. It is complete enough to call correctly, though it omits any sense of response format or depth.

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 takes zero parameters, so per the rubric the baseline is 4. There is nothing for the description to disambiguate, and it does not introduce any parameter-like inputs.

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 states a concrete verb and resource: it returns the main ways to gather Google Play closed-testing testers, including typical costs and cautions, and explicitly scopes itself as general guidance with a short note about Minna no Tester at the end. This is clearly distinguishable from siblings like list_recruiting_apps or check_optin_link, which are operational rather than explanatory.

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?

It gives explicit trigger conditions in both languages: use it when the user asks how to gather testers for closed testing or says they cannot get enough testers. It does not name alternative tools or state when NOT to use it, so it falls short of full routing guidance but the when-to-use is unambiguous.

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?

Annotations already declare this as a read-only, idempotent, non-destructive, closed-world list operation. The description adds meaningful behavioral context: every tester count must include its source and date, joining a test requires website signup, and remaining deposited credits are not a tester cap. Return ordering/pagination is not covered, but the core behaviors are well disclosed.

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 purpose, then usage, then behavioral caveats. The bilingual Japanese/English duplication slightly weakens conciseness, and the English version omits one caveat present in Japanese, but the information remains reasonably tight.

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 no-parameter, read-only list tool with annotations covering safety, the description supplies the important missing context: what the list represents, when to use it, how to relay tester counts, and the signup prerequisite. It does not fully specify pagination or complete return structure, but no output schema exists and the essential agent-facing context is present.

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 zero parameters, so the baseline is 4. No parameter-level semantics are needed beyond the description of what the returned list contains.

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?

States a specific verb and resource: returns a list of apps currently recruiting testers on Minna no Tester. It is clear enough that an agent can distinguish this from service-info or opt-in-link tools, though it does not explicitly name those siblings as 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?

Gives explicit usage context: use when the user asks which apps they can test now or wants to participate as a tester. It lacks when-not guidance or direct references to sibling tools, but the intended trigger is clear.

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