sukimalabo-tester

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

Annotations already declare readOnlyHint=true and openWorldHint=false, and the description is consistent with both. It adds genuine context beyond the annotations: the check is free, the verdict comes with a reason and remediation guidance, results are returned bilingually, and existence/liveness of the test is explicitly out of scope.

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 bilingual pairing doubles the length, but that is intentional and useful for a JP/EN-facing tool, and each half is front-loaded with the core purpose followed by the limitation. Every sentence carries information; there is no 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?

With no output schema, the description carries the return-value burden and discharges it: the agent learns it receives a determination plus a reason and a fix, in two languages. Combined with the scope limits, an agent has everything needed to call this tool 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 coverage is 100% and the single required 'url' parameter is already documented with an example, so the schema does the heavy lifting. The description adds the acceptable URL shape (https://play.google.com/apps/testing/<package>) but no additional format constraints or validation rules, making the baseline 3 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 (診断/checks the format) and resource (Google Play testing opt-in URL), and goes further by naming the categories it discriminates between: closed test vs. internal test vs. public store page. An agent can distinguish this from siblings like list_recruiting_apps or how_to_get_testers without opening any 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?

The description gives clear framing (a free format diagnosis to run on a candidate opt-in URL) plus an explicit boundary: it does NOT verify that the test exists or is currently open. That boundary tells the agent when this tool is insufficient, but no sibling alternative is named for the cases this tool cannot cover, so it falls short of a 5.

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=true and openWorldHint=false, so safety is covered. The description adds genuinely new context: numbers are read as the latest values from operator configuration (freshness guarantee) and the response explicitly includes what the service does not promise, which is useful expectation-setting not derivable from the schema.

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-loaded with what is returned, then supporting context. The bilingual JA/EN content is somewhat redundant since the English largely translates the Japanese, and the 'latest values from settings' note appears only in Japanese. Still, every part carries information and nothing is padded.

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 parameters and no output schema, the description carries the burden of explaining the return content, and it does so (overview, mechanism, intake/sales status, non-promises). Freshness semantics are covered. An agent has enough to call and interpret it correctly, though return shape specifics remain unstated.

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 nothing for the description to disambiguate. Baseline 4 applies; the description does not need to add parameter 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?

States a specific return: an overview of 'Minna no Tester', its mechanism, current intake/sales status, and what it does NOT promise. The resource is clearly named and the service context (Google Play closed-test mutual testing, 12 testers x 14 days) is spelled out. It does not explicitly contrast itself with the sibling tools (check_optin_link, how_to_get_testers, list_recruiting_apps), so it stops short of the sibling-differentiating bar.

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?

Usage is implied rather than stated: an agent can infer this is the entry-point overview and status check, and the 'what it does NOT promise' phrasing hints at expectation-setting. However, there is no explicit 'use this when…' or 'use X instead' routing against the sibling tools, leaving the choice to inference.

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=true and openWorldHint=false, so safety is covered. The description adds valuable content disclosure beyond that: the answer includes costs and cautions, and covers the 12-tester/14-day requirement. It does not describe output format, but the annotations carry the behavioral load.

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-loaded with the core purpose and scope, then the use condition. The full bilingual restatement (Japanese plus English) is somewhat duplicative, but it serves a bilingual audience without obscuring the content.

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 zero-parameter advisory tool with no output schema and annotations covering the read-only/no-network profile, the description is sufficient: it tells the agent what knowledge the tool returns and when to reach for it.

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 nothing for the description to disambiguate; baseline 4 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?

States a specific verb (explains/gather) and resource (testers for Google Play closed testing) with concrete scope (12 testers x 14 days). It is clearly distinct from the sibling tools (check_optin_link, get_service_info, list_recruiting_apps), which are operational rather than advisory.

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?

Explicitly names the situation for use: individual app developers who want to know how to gather testers. No alternative tools are named, but the triggering condition 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 cover read-only and non-open-world behavior, so the description's added value is notable: it discloses that tester counts always come with provenance and date, and warns that a remaining prepaid count is not an upper bound. Minor gaps remain around pagination, ordering, or freshness, but the core behavioral caveats are well covered.

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 the purpose and then adds operational caveats. It is bilingual, which creates some duplication, and the English summary omits the cap caveat present in Japanese, but overall the content is relevant and not padded.

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 zero-parameter, read-only listing tool with no output schema, the description supplies what an agent needs: what is returned, the provenance contract for tester counts, and an important interpretation warning about upper bounds. Nothing essential for correct invocation is missing.

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 and the schema is an empty object, so there is no parameter semantics burden on the description. The baseline for a parameterless tool is 4.

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 specific verb and resource: it lists apps that are currently recruiting testers. This is clear enough to distinguish the tool semantically from siblings like get_service_info and how_to_get_testers, but it does not explicitly name those alternatives or draw the contrast.

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 implied usage: use this to retrieve recruiting listings, and preserve source/date when relaying counts. However, it does not say when to call this instead of check_optin_link, get_service_info, or how_to_get_testers, nor does it give exclusions.

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