SEO対策研究室 MCP

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

With no annotations provided, the description carries the full burden. It does disclose that the tool returns a composite of several distinct pieces of content (site overview, profile, privacy policy, verification cases), which is a behavioral trait. However, it does not mention whether this is a read-only operation, if authentication is required, or the structural format of 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?

The description is a single, information-dense sentence that front-loads the exact output contents. Every element listed is relevant, with no redundant or vague 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?

Given the tool's simplicity (0 parameters, no output schema), the description adequately covers what is returned by explicitly naming all four components. It could be enhanced with ordering or format details, but the current scope is sufficient for an agent to decide whether to invoke 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 has zero parameters, so parameter semantics are not applicable. According to the rubric, a baseline of 4 is appropriate when no parameters exist, and the description adds no conflicting information.

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 uses the specific verb '返します' (returns) and clearly enumerates the resources: site overview, administrator (柏崎剛) profile, privacy policy, and verification case list. This precisely defines the tool's output and distinguishes it from sibling tools like get_services or search_content, which focus on different resource types.

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 when-to-use guidance or alternatives are mentioned. The purpose is clear from the name and description, implying that this tool is for retrieving site/profile information, but it lacks explicit exclusions such as 'for services, use get_services instead.'

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 of disclosing behavioral traits. It states that the tool returns a list with descriptions and canonical URLs, which gives some transparency about the output. However, it does not mention side effects, authentication requirements, pagination, or whether it is read-only. For a simple get-list tool, this is adequate but not comprehensive.

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 a single sentence that is front-loaded with the main action ('Returns a list of initiative pages...'). It is concise, contains no fluff, and every word earns its place. The structure immediately clarifies the tool's 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?

Given the tool's low complexity (0 params, no output schema), the description adequately explains the return value: a list of pages with descriptions and canonical URLs. It does not detail the exact response structure or pagination, but these are not critical for a simple list retrieval. The main gap is the lack of usage context, already covered in 'usage_guidelines'.

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 input schema is an empty object, so schema description coverage is trivially 100%. Per the rubric, 0 params yields a baseline score of 4, and the description does not need to add parameter details. It adds no parameter information, but none is needed.

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's function: it returns a list of initiative pages (業種別SEO・教育SEO等) with their descriptions and canonical URLs. The verb '返します' (returns) and the specific resource make the purpose unambiguous. It distinguishes itself from sibling tools by focusing on the 'services/取り組み' list, not content reading or searching.

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 provides no explicit guidance on when to use this tool versus alternatives. It does not mention any exclusions or alternatives (e.g., 'use search for specific queries'). The usage context must be inferred entirely from the tool name and description, which is minimal.

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 are provided, so the description must carry the behavioral burden. It does disclose the ordering and scope ('official content'), but it does not mention default limit behavior, pagination, or how the optional content_types filter affects results. The read-only nature is implicit but not stated.

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 compact sentences deliver the essential action and purpose with no repetition or filler. The main verb and ordering are front-loaded, making it easy to scan.

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 tool is relatively simple with three optional parameters, but with no annotations and no output schema, the description should provide more operational context. It covers ordering and purpose but omits the output shape, default limits, and how content_types modifies the result. This is adequate but has clear gaps.

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 description adds no parameter-level meaning. Schema coverage is only 33% (only 'since' has a description), and limit and content_types remain completely undocumented. The description does not compensate for this gap; it only mentions 'without search terms,' which does not clarify any of the three 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 uses a specific verb ('一覧します' / lists) and a specific resource ('公式コンテンツ' / official content), while also specifying the ordering (newest by update date). The phrase '検索語なしで' (without search terms) clearly distinguishes it from the sibling search tool.

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 the use case: checking recent official content without a search query. This provides clear contextual guidance and implies that the search sibling should be used when a search term is present. It does not explicitly name alternatives or exclusions, so it does not reach 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?

With no annotations provided, the description carries the burden of behavioral disclosure. It indicates a read-only operation returning full text and metadata, but does not describe the effect of max_chars, error behavior when both or neither identifier is provided, or any access restrictions beyond 'public'.

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 a single concise sentence with no filler. It front-loads the main purpose and immediately clarifies identification method, making it easy to parse.

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 single-item read tool, the description covers the core purpose and identification, but lacks details about max_chars, output shape, and edge cases. Since there is no output schema and no annotations, the description should have been more explicit about these behaviors.

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 only partially compensates. It explains that content_id and url are alternative ways to specify the item, but completely omits max_chars and does not clarify precedence, requirements, or truncation behavior.

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 full text and metadata for a single public content item, specifying the resource and action. The phrase 'content_id か canonical URL で指定します' distinguishes it from sibling tools that list, search, or research content.

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 usage when you need full text of one specific public content item and know its content_id or canonical URL. However, it provides no explicit comparison to sibling tools like list_recent_tsuyoshikashiwazaki_content or search_tsuyoshikashiwazaki_content, nor any 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains that evidence is classified into categories, confidence is attached, and a special fallback (confidence='none') is returned when no evidence exists. This is meaningful behavioral context beyond the basic read-only implication.

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 a single, well-structured sentence that front-loads the purpose and adds behavioral details without redundancy. Every clause contributes meaning, and there is no wasted text.

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 absence of an output schema and annotations, the description should explain return structure and parameters more fully. It covers the core behavior and fallback, but leaves 'max_evidence' undocumented and does not specify the exact response format beyond classification and confidence, making it only minimally 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 description coverage is 0%, so the description must compensate. It only implicitly references 'topic' via '特定テーマ' but never explains the 'max_evidence' parameter, its purpose, or its relationship to the result. This leaves the agent without sufficient understanding of how to control the evidence count.

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 a specific verb and resource: '調べます' (investigate) the viewpoint/explanations of SEO対策研究室 for a specific theme. It distinguishes itself from sibling tools like search_content by emphasizing evidence classification and confidence levels, making its unique purpose evident.

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 clearly implies usage context: call this tool when you need the site owner's viewpoint on a specific theme ('特定テーマに対する...考え方・解説'). It does not explicitly name alternatives or exclusions, but the context is clear enough to guide selection among 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 carries the burden of behavioral disclosure. It clearly states the tool performs a search and explicitly lists the output fields (canonical URL, content type, source classification, update date, relevant passage), which implies a read-only operation. It does not mention pagination, ordering, or exact matching behavior, but these details are not critical for basic use and the core behavior is well communicated.

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, using two sentences to convey the purpose, scope, content types, and result fields without redundancy. Operator and site details are efficiently placed in a parenthetical, keeping the main action front-loaded. Every part serves a 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?

Given there is no output schema, the description appropriately lists the result fields, making the expected return values clear. Combined with the well-documented input schema (83%), the description provides enough context for correct invocation. Some nuances like default ordering, pagination, or handling of empty results are absent, but these are not required for basic use of a search 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?

Schema description coverage is 83% (5 of 6 parameters have descriptions). The description adds meaning beyond the schema by mapping the abstract content_types enum values to concrete content categories (e.g., 'SEO解説記事', '用語辞典', '検証事例'), helping the agent understand what each enum represents. The 'limit' parameter remains undocumented in both the schema and description, but this is a minor gap given the high coverage and extra value added.

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 cross-search ('横断検索') across specific content types of the SEO対策研究室, enumerating the exact categories and stating the result fields (canonical URL, type, source, update date, relevant sections). It distinguishes itself from sibling tools like read_tsuyoshikashiwazaki_content and list_recent_tsuyoshikashiwazaki_content by focusing on search rather than retrieval of individual or recent items.

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 provides clear context by defining the search scope (public information of the site) and listing content types, which implies when this tool is appropriate (finding content across the site). However, it does not explicitly mention alternatives or exclusions relative to sibling tools, so the 'vs alternatives' aspect is only implied but not explicitly stated.

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