Prenica（プレニカ）

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

The annotations already mark the tool as read-only and non-destructive. The description adds that the full body is returned from beginning to end, which is useful, but it does not disclose any additional behavioral caveats such as response size, formatting, or limitations.

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 short sentences with no filler: the first states the core return behavior, the second gives the concrete use case. The most important information is front-loaded.

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 read-only fetch with one fully documented parameter and an output schema, the description covers the essential behavior and intended usage. It could be slightly stronger with explicit sibling differentiation, but nothing required to invoke the tool correctly 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 sole id parameter is already fully documented in the schema, including that it accepts a URL or path and even Japanese text. The description adds no parameter-specific meaning beyond tying the id to search results, so the baseline of 3 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 states a concrete action—returning the full body text of a Prenica page—rather than restating the tool name. It is clear and specific, but it does not explicitly differentiate itself from siblings such as get_page or search_pages.

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 a concrete trigger: use this tool when reading press-release details found via search, listing fields like announcement content, date/time, place, price, and contact info. It does not mention when not to use it or name alternatives, so it stops short of a full 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 cover the safety profile (readOnly, not destructive). The description adds the useful context that this is a legacy alias and that the return format differs from fetch, but it does not specify what that format actually is, which is a notable but not severe omission.

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 compact and front-loaded with the key facts: legacy status, purpose, and the instruction to use fetch. The phrase 'returned form is different' is vague, but every sentence otherwise earns its place and there is no padding.

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 one-parameter read-only tool with safety annotations, most needed context is present. However, since there is no output schema and the description explicitly highlights a different return shape without explaining it, an agent cannot fully anticipate the response structure, leaving a real gap.

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 sole parameter, url, is already fully documented in the schema with 100% coverage, including that Japanese paths are allowed. The description adds no additional parameter-level meaning, so the schema carries the burden and 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?

The description clearly identifies the tool as a legacy version of fetch that returns page contents, and it names fetch as the current equivalent. It distinguishes the tool from its siblings by pointing to the different return shape, though the exact difference is left unspecified.

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 says this tool exists for people already connected and instructs new users to use fetch instead. This is direct when-to-use / when-not-to-use guidance with the named alternative, so an agent can decide correctly without further research.

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 mark the tool as read-only and non-destructive, so the safety profile is covered. The description adds genuine value beyond annotations by disclosing the staleness behavior ('読み込んだ時点のもので、最新のリリースがすべて入っているとは限りません'), which is important for an agent to interpret results correctly.

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 with the core purpose front-loaded, followed by useful taxonomy detail and the staleness caveat. Each sentence earns its place with minimal waste; slightly verbose URL explanation but 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?

For a read-only list tool with no output schema, it covers the return value (titles and URLs) and the staleness caveat well. However, the limit/offset parameters are the agent's only control over the listing and are entirely undocumented, leaving pagination behavior unclear.

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 never mentions limit or offset, leaving both pagination parameters entirely unexplained. With two parameters and no schema descriptions, the description should compensate but adds nothing about parameter meaning or usage.

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: 'Returns a list (titles and URLs) of pages loaded into Prenica.' It also differentiates from siblings by detailing the URL taxonomy (/news/, /type/, /about/), so an agent can distinguish it from search_pages, fetch, and get_page without opening schemas.

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 implies the use case (enumerate all loaded pages) and adds a useful caveat that the list is as-of load time and may not contain the latest releases. However, it never explicitly states when to prefer this over siblings such as search_pages or get_page, leaving selection 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/openWorldHint=false/destructiveHint=false, so the safety profile is covered structurally. The description adds that results are excerpted and instructs the agent to include company name and page URL when answering, which is useful output guidance but not deep behavioral disclosure (no ranking, pagination, or scope limits).

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 front-loaded paragraph that moves from purpose to query types to examples to answer conventions. It is somewhat long but every clause carries information; no filler or 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?

With an output schema present, the description correctly need not explain return values, and it covers the one parameter and usage context well. The remaining gap is sibling differentiation — it never mentions search_pages or how this differs from 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?

Schema coverage is 100% with a single query parameter, so baseline is 3. The description goes beyond the schema's short hint by illustrating how composite keywords map to searches (e.g. 「セミナー 10月」「イベント 開催」), adding real semantic value for constructing the query.

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 names a specific verb (探す) and resource (press releases hosted on 「Prenica」), and clarifies results come with excerpts. It is clearly not a generic web search. However it does not distinguish itself from the sibling search_pages, leaving ambiguity about which search to pick.

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 concrete context on what kinds of queries work (company/product/event names, industry keywords) and even example query strings like 「セミナー 10月」「コスメ」, and notes it also answers questions about pricing/publication standards. But it never says when to use this over the sibling search_pages or list_pages, so routing guidance is only implied.

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 signal read-only and non-destructive behavior. The description adds legacy status and the fact that behavior matches 'search' but with a different return shape, which is useful context beyond annotations. However, it does not disclose any additional behavioral details (e.g., performance, rate limits) that might matter.

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 concise sentences, each earning its place: the first establishes the legacy context and the second clarifies the relationship to 'search' and gives explicit usage guidance. The critical guidance (use search) is front-loaded and repeated, making it easy for an agent to act on.

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 legacy alias tool with no output schema, the description explains the tool's purpose and routing clearly, but omits the specific return format difference, which would be needed for an agent to fully understand invocation results. Given the tool's deprecated nature and the explicit push toward 'search', this is adequate but not wholly 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?

The schema provides a description for 'query' (50% coverage), and the 'limit' parameter is documented via its default/min/max constraints. The description adds no parameter-specific semantics beyond saying it searches 'like search', offering no details on how query or limit behave differently or are formatted. With only 50% schema coverage, the description should compensate but does not.

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 action (search inside the site) and resource (prenica.jp), and explicitly distinguishes itself from the sibling 'search' by noting the return format is different. However, it does not specify what the return format actually is, leaving slight ambiguity about the exact output.

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 states to use 'search' for new usage ('新しく使うときは search を使ってください'), clearly identifying the alternative and the condition under which this legacy tool should be used. This leaves no ambiguity about when to invoke this tool versus 'search'.

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 mark readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds useful context about what the overview contains—free, review-based, verified corporations, and pre-publication checks—helping the agent set expectations for the 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?

Three short sentences, front-loaded with the core action, followed by relevant detail and a usage condition. There is no filler or redundant information.

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 overview tool with no output schema, the description fully covers what the tool does, what content to expect, and when to call it. Nothing essential 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?

There are zero parameters and schema coverage is trivially 100%, so the baseline for zero-param tools applies. The description does not need to explain parameters.

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 opens with a specific verb and resource: '「Prenica」の概要を返します' (returns the Prenica overview). It then clarifies the content and closes with the exact use case, distinguishing it from sibling tools like fetch or search_pages.

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 states when to use the tool: 'どんなサービスかを最初に知りたいときに使います' (use when you first want to know what the service is). It does not name sibling exclusions, but the use case is clear enough to route an agent correctly.

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