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

No annotations are provided, so the description carries the full burden. It states the tool creates a draft email, implying a write operation, but doesn't disclose behavioral traits such as permissions required, whether drafts are saved automatically, error handling, or rate limits. The mention of content reuse from 'get_email' adds some context, but critical mutation behaviors are undocumented.

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 and front-loaded: the first sentence states the core purpose, and the second provides a usage tip. Both sentences earn their place by adding value. However, it could be slightly more structured by explicitly separating purpose from guidelines.

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 complexity (7 parameters with nested objects, no output schema, and no annotations), the description is incomplete. It doesn't explain what happens after creation (e.g., draft ID returned, storage location), error conditions, or how parameters like 'content' and 'htmlBody' interact. For a mutation tool with rich inputs, more behavioral context is needed.

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 schema already documents all 7 parameters thoroughly. The description adds minimal value beyond the schema: it hints that 'content' can be obtained from 'get_email' for cloning, but doesn't explain parameter interactions or usage semantics. With high schema coverage, the baseline score of 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 states the tool's purpose: 'メールの下書きを作成' (create a draft email). It specifies the verb (create) and resource (draft email), but doesn't explicitly differentiate from sibling tools like 'update_email' beyond mentioning 'get_email' for content reuse. The purpose is clear but sibling differentiation is limited to content sourcing.

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 implied usage guidance: it suggests using 'get_email' to obtain content when cloning existing emails. However, it doesn't specify when to use this tool versus alternatives like 'update_email' or 'list_emails', nor does it mention prerequisites or exclusions. The guidance is helpful but incomplete.

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 full burden for behavioral disclosure. It states this is a retrieval operation ('取得'), which implies read-only behavior, but doesn't specify authentication requirements, rate limits, error conditions, or what happens if the email ID is invalid. For a tool with zero annotation coverage, this leaves significant behavioral gaps.

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, efficient Japanese sentence that directly states the tool's purpose with zero wasted words. It's appropriately sized for a simple retrieval tool and front-loads the essential information. Every word earns its place.

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 retrieval tool with no annotations and no output schema, the description is insufficient. It doesn't explain what '詳細情報' (detailed information) includes, the response format, error handling, or authentication needs. Given the lack of structured fields to compensate, the description should provide more complete context about the tool's behavior and outputs.

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%, with the single parameter 'emailId' clearly documented in the schema as 'メールID'. The description doesn't add any parameter information beyond what the schema provides - it doesn't explain where to find email IDs, their format, or validation rules. Baseline 3 is appropriate when the schema does the heavy lifting.

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 action ('取得' - get/retrieve) and the resource ('特定のメールの詳細情報' - specific email's detailed information). It distinguishes from sibling tools like 'list_emails' (which lists multiple emails) and 'create_draft_email' (which creates new emails). However, it doesn't explicitly mention what constitutes 'detailed information' beyond the basic email 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 provides no guidance on when to use this tool versus alternatives like 'list_emails' or 'update_email'. There's no mention of prerequisites (e.g., needing an email ID), use cases (e.g., viewing full email content after listing), or exclusions (e.g., not for modifying emails). The agent must infer usage from the tool name and sibling context alone.

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 full burden for behavioral disclosure but offers minimal information. It states it 'gets' marketing emails but doesn't clarify whether this is a read-only operation, whether it requires specific permissions, what format the returned data takes, or if there are rate limits. For a list operation with zero annotation coverage, this represents a significant transparency gap.

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 extremely concise - a single Japanese sentence that directly states the tool's purpose without any fluff or unnecessary elaboration. Every word serves a functional purpose, making it efficient and front-loaded with the essential information about what the tool does.

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 list operation with no annotations and no output schema, the description is insufficiently complete. While the purpose is clear, there's no information about what the tool returns (email objects, IDs only, metadata), how results are structured, or any behavioral constraints. The agent lacks crucial context needed to properly invoke and interpret results from this 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 description adds no parameter information beyond what's already documented in the schema (which has 100% coverage). All three parameters (limit, offset, sort) are fully described in the input schema with their types, defaults, and format details. The description doesn't provide additional context about how these parameters affect the listing behavior, so it meets the baseline for high schema coverage.

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 action ('取得' - get/fetch) and resource ('HubSpotのマーケティングメール一覧' - HubSpot marketing email list), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_email' (which likely fetches a single email) or 'create_draft_email', leaving room for potential confusion about when to use this list operation versus individual email retrieval.

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 guidance on when to use this tool versus alternatives. There's no mention of when to choose list_emails over get_email (for bulk vs single email retrieval) or create_draft_email. The agent must infer usage context solely from tool names, which is insufficient for optimal tool selection.

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 but offers nothing beyond the basic action. It doesn't indicate whether this is a destructive operation, what permissions might be required, whether changes are reversible, or what happens to unspecified fields. For a mutation tool with zero annotation coverage, this represents a critical gap in understanding the tool's behavior.

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 extremely concise at just two words ('メールを更新'), with zero wasted language. It's front-loaded with the core action and resource. While this conciseness comes at the expense of completeness, the description itself doesn't contain unnecessary verbiage or poor structure.

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 this is a mutation tool with 5 parameters (including nested objects), no annotations, and no output schema, the description is completely inadequate. It provides no context about what 'updating' entails, what fields can be modified, what the response looks like, or how this differs from creation operations. The agent would struggle to use this tool correctly without significant trial and error.

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 description coverage is 100%, with all 5 parameters documented in the input schema. The description adds no additional parameter information beyond what's already in the structured schema. According to scoring rules, when schema coverage is high (>80%), the baseline score is 3 even with no parameter information in the description.

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 'メールを更新' (Update email) is a tautology that restates the tool name without providing specific details about what aspects of an email are updated. It doesn't distinguish this tool from sibling tools like create_draft_email or get_email beyond the basic verb. While it identifies the resource (email), it lacks specificity about scope or functionality.

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 guidance on when to use this tool versus alternatives like create_draft_email or get_email. There's no mention of prerequisites, appropriate contexts, or exclusions. The agent receives no help in distinguishing between update operations and creation/retrieval operations.

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