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

No annotations are provided, so the description carries full burden. It states the action but doesn't disclose behavioral traits like whether this requires specific permissions, if reactions are reversible, rate limits, or what happens on success/failure. For a mutation tool with zero annotation coverage, this is a significant 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 a single, efficient sentence in Korean that directly states the tool's purpose. It's appropriately sized and front-loaded with zero wasted words, making it easy to understand quickly.

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 (adding reactions) with no annotations and no output schema, the description is incomplete. It doesn't cover behavioral aspects like permissions, side effects, or response format. For a tool with 3 parameters and no structured safety hints, more 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 three parameters (channel_id, timestamp, reaction) with clear descriptions. The description adds no additional parameter semantics beyond what the schema provides, but the baseline is 3 when schema coverage is high.

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 ('메시지에 이모지 반응 추가' translates to 'Add emoji reaction to message') with specific verb+resource. It distinguishes from siblings like posting messages or getting channel history by focusing on reactions. However, it doesn't explicitly differentiate from potential similar tools (though none exist in the sibling list).

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 guidance on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., needing message access), exclusions, or comparisons with sibling tools like slack_post_message. Usage is 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.

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 states the action but lacks details on permissions, rate limits, pagination, or response format. This is a significant gap for a tool that likely involves API calls and data retrieval.

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, clear sentence with no wasted words. It's front-loaded and efficiently conveys the core purpose without unnecessary elaboration.

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 lack of annotations and output schema, the description is incomplete. It doesn't address behavioral aspects like authentication needs, error handling, or what the returned messages look like, which are crucial for an agent to use this tool effectively in a Slack context.

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 both parameters (channel_id and limit) adequately. The description doesn't add any additional meaning or context beyond what the schema provides, which meets the baseline for high 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 '채널에서 최근 메시지 가져오기' clearly states the action (가져오기/fetch) and resource (채널 메시지/channel messages), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'slack_get_thread_replies' or 'slack_post_message', which prevents a perfect score.

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. It doesn't mention context like retrieving general channel history versus thread-specific replies or user profiles, leaving the agent to infer usage from tool names 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. It states what the tool does but doesn't describe important behaviors: whether it's read-only (implied by '가져오기' but not explicit), pagination/rate limits, authentication requirements, error conditions, or what format the replies are returned in. For a tool with zero annotation coverage, this is insufficient.

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 sentence in Korean that directly states the tool's purpose. There's zero wasted language or redundancy. It's appropriately sized for a simple retrieval tool and gets straight to the point.

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 no annotations and no output schema, the description is incomplete. It doesn't explain what '가져오기' returns (reply objects, just text, metadata?), error handling, or operational constraints. For a tool that presumably returns multiple items (replies), more context about the response format and behavior would be helpful.

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 both parameters are well-documented in the schema. The description adds no additional parameter information beyond what's already in the schema (channel_id and thread_ts are fully described there). This meets the baseline of 3 when schema does the heavy lifting, but adds no extra value.

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 ('가져오기' - get/fetch) and resource ('메시지 스레드의 모든 답장' - all replies in a message thread). It distinguishes from siblings like slack_get_channel_history (general channel history) and slack_reply_to_thread (posting replies). However, it doesn't explicitly mention Slack as the platform, though this is implied by the tool name.

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. It doesn't mention prerequisites (like needing a specific channel or thread), nor does it differentiate from similar tools like slack_get_channel_history which might also retrieve thread content. There's no 'when-not' or alternative tool recommendation.

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 carries full burden for behavioral disclosure. It states the tool fetches data ('가져오기'), implying a read-only operation, but doesn't specify authentication requirements, rate limits, error conditions, or what happens if the user 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, clear sentence in Korean that directly states the tool's function without unnecessary words. It's appropriately sized for a simple tool, though it could be slightly more informative without losing 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?

Given no annotations, no output schema, and a simple input schema, the description is incomplete. It lacks details on return values (e.g., what profile fields are included), error handling, or behavioral traits like permissions. For a tool fetching user data in a collaborative platform like Slack, more context is needed to ensure proper usage.

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 input schema has 100% description coverage, with 'user_id' documented as '사용자의 ID' (user's ID). The description doesn't add any parameter-specific details beyond what the schema provides, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't need to.

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 the action ('가져오기' - get/fetch) and target ('사용자의 상세 프로필 정보' - detailed user profile information), providing a basic purpose. However, it doesn't differentiate from sibling tools like 'slack_get_users' (which likely lists multiple users) or specify what constitutes 'detailed' profile information versus basic user data.

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 guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., needing a user ID), exclusions, or comparisons to siblings like 'slack_get_users' for bulk retrieval or other profile-related tools. Usage context is implied but not explicit.

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 states what the tool does but doesn't describe key behavioral traits such as pagination behavior (implied by the cursor parameter but not explained), rate limits, authentication needs, or what '기본 프로필 정보' (basic profile information) specifically includes. This leaves significant gaps for an agent to understand how to use it effectively.

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 sentence in Korean that directly states the tool's purpose without any fluff or redundancy. It's appropriately sized and front-loaded, making it easy to parse quickly.

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 of a user-fetching tool with pagination (2 parameters) and no annotations or output schema, the description is incomplete. It doesn't explain the return format, pagination mechanics beyond the cursor parameter, or error handling, which are crucial for an agent to use this tool correctly in a Slack workspace context.

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 input schema has 100% description coverage, with clear documentation for both parameters (cursor for pagination, limit with default and max). The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline of 3 where 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 ('가져오기' - get/fetch) and resource ('워크스페이스의 모든 사용자와 기본 프로필 정보' - all users and basic profile information in the workspace), providing a specific verb+resource combination. However, it doesn't explicitly distinguish this tool from sibling tools like 'slack_get_user_profile' which might fetch a single user's profile, leaving some ambiguity about sibling differentiation.

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. It doesn't mention when to use this bulk user-fetching tool instead of 'slack_get_user_profile' for individual profiles, or any prerequisites like authentication requirements, leaving the agent with no usage context.

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. It discloses pagination behavior ('페이지네이션과 함께' - with pagination), which is valuable context not in the schema. However, it doesn't mention rate limits, authentication needs, whether this is a read-only operation, or what the return format looks like. For a listing tool with no annotations, 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 sentence that front-loads the core purpose. Every word earns its place by specifying the resource scope (public/pre-defined channels) and key behavior (listing with pagination). No wasted words 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?

Given the tool's moderate complexity (listing with pagination), no annotations, and no output schema, the description is minimally adequate. It covers the basic purpose and pagination behavior but lacks details about authentication, rate limits, return format, error conditions, or how it differs from sibling tools. For a tool with no structured safety or output information, more context would be helpful.

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 fully documents both parameters (limit and cursor). The description adds no additional parameter semantics beyond what's in the schema. It mentions pagination generally but doesn't explain how the cursor parameter works or provide usage examples. Baseline 3 is appropriate when the schema does all the work.

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 ('목록으로 표시' - list/display) and resource ('workspace의 public 또는 pre-defined된 채널' - public or pre-defined channels in workspace). It distinguishes from siblings by focusing on listing channels rather than posting messages, getting history, or managing users. However, it doesn't explicitly differentiate from potential similar listing tools that might exist.

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 context by specifying 'public 또는 pre-defined된 채널' (public or pre-defined channels), suggesting this is for viewing available channels rather than private ones. However, it doesn't provide explicit guidance on when to use this vs. alternatives like slack_get_users or slack_get_channel_history, nor does it mention prerequisites or 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 full burden for behavioral disclosure. While '게시' (post) implies a write operation, it doesn't specify permissions needed, rate limits, whether the message is ephemeral or permanent, or what happens on failure. This is inadequate for a mutation tool with zero annotation coverage.

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 sentence in Korean that directly states the tool's purpose with zero wasted words. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly.

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 mutation tool with no annotations and no output schema, the description is insufficient. It doesn't cover behavioral aspects like authentication requirements, error handling, or response format. Given the complexity of posting to Slack (which involves permissions, formatting options, etc.), more 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 fully documents both parameters (channel_id and text). The description adds no additional parameter information beyond what's in the schema, such as format examples or constraints. Baseline 3 is appropriate when schema does all the work.

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 ('게시' meaning 'post') and resource ('Slack 채널에 새 메시지' meaning 'new message to Slack channel'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like slack_reply_to_thread or slack_add_reaction, which also involve posting content to Slack.

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. It doesn't mention when to use slack_post_message versus slack_reply_to_thread for threaded replies, or slack_add_reaction for reactions, leaving the agent without context for 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. It states the tool replies to a thread, implying a write operation, but lacks details on permissions required, rate limits, error conditions, or what happens if the thread doesn't exist. This is a significant gap for a mutation tool with zero annotation coverage.

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 sentence in Korean that directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, making it easy to understand at a glance.

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 complexity (a write operation with no annotations and no output schema), the description is incomplete. It lacks behavioral details like success/error responses, side effects, or integration with sibling tools. While the schema covers parameters well, the overall context for safe and effective use is insufficient.

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%, so the schema already documents all three parameters thoroughly. The description adds no additional meaning beyond what the schema provides, such as explaining parameter relationships or usage examples. 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 ('답장' meaning 'reply') and the target resource ('특정 메시지 스레드' meaning 'specific message thread') in Slack. It distinguishes from sibling tools like 'slack_post_message' (general posting) by specifying thread replies, though it doesn't explicitly contrast with other siblings like 'slack_add_reaction'.

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. It doesn't mention prerequisites (e.g., needing a thread to reply to), exclusions, or comparisons to siblings like 'slack_post_message' for non-threaded messages or 'slack_get_thread_replies' for reading instead of writing.

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