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

No annotations are provided, so the description carries full burden. It mentions that results include author names and usernames automatically, which adds some behavioral context. However, it doesn't disclose critical traits like whether this is a read-only operation, rate limits, authentication needs, pagination behavior beyond parameters, or error handling. For a tool with no annotations, this leaves significant 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?

Two concise sentences with no wasted words. The first sentence states the core purpose, and the second adds a useful behavioral detail. It's appropriately sized and front-loaded, though could be slightly improved with more structure (e.g., bullet points for clarity).

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 return values beyond mentioning included fields, nor does it cover error cases, permissions, or side effects. For a tool with 3 parameters and retrieval functionality, more context is needed to ensure the agent can use it effectively.

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 parameters (channel_id, page, per_page) with descriptions and defaults. The description adds no additional parameter semantics beyond what the schema provides, such as format details or constraints. Baseline 3 is appropriate when 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 ('가져옵니다' - fetches/retrieves) and resource ('특정 채널의 최근 메시지들' - recent messages from a specific channel). It distinguishes from siblings like get_channels (which lists channels) and search_messages (which searches across channels), though not explicitly. However, it doesn't fully differentiate from get_post_thread (which might retrieve thread messages) or search_user_messages (user-specific), so it's not a perfect 5.

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 guidance on when to use this tool versus alternatives like search_messages or get_post_thread. The description implies it's for recent messages in a specific channel, but doesn't specify contexts like 'use this for quick overviews' or 'avoid for historical searches.' Without such distinctions, the agent lacks clear decision criteria.

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 only states what the tool does at a high level ('fetches channel list') but doesn't describe return format, pagination behavior, authentication requirements, rate limits, error conditions, or whether this is a read-only operation. For a tool with zero annotation coverage, this is insufficient behavioral context.

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's appropriately sized for a simple retrieval tool and front-loads the essential 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 tool with no annotations, no output schema, and minimal behavioral description, this is incomplete. The description doesn't help an agent understand what format the channel list returns, whether it includes metadata, how to handle large result sets, or what permissions are required. Given the lack of structured data, the description should provide more operational 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 the single parameter (team_id). The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain what constitutes a valid team_id, where to find it, or provide examples. Baseline 3 is appropriate when the schema does the documentation 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 ('가져옵니다' - fetches/retrieves) and resource ('채널 목록' - channel list) with scope ('특정 팀의' - for a specific team). It distinguishes from siblings like get_channel_messages (which gets messages) and get_teams (which gets teams rather than channels). However, it doesn't explicitly differentiate from all possible list operations in the sibling set.

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 get_channels versus search_messages for finding channels, or whether this is the primary way to list channels versus other methods. No prerequisites, exclusions, or comparative context is provided.

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 ('조회합니다'), which implies read-only behavior, but doesn't address authentication requirements, rate limits, error conditions, or what specific user information is returned. The description is minimal and lacks important operational context.

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 directly states the tool's purpose without unnecessary elaboration. It's appropriately sized for a simple retrieval tool with no parameters, though it could potentially be more front-loaded with key 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 user information retrieval tool with no annotations and no output schema, the description is insufficient. It doesn't specify what user information is returned (profile data, permissions, etc.), format of response, or any limitations. The description should provide more context about the return value and operational constraints.

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 0 parameters with 100% schema description coverage, so the schema already fully documents the parameter situation (none). The description appropriately doesn't discuss parameters, maintaining focus on the tool's purpose. Baseline for 0 parameters 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 clearly states the action ('조회합니다' - retrieve/query) and resource ('현재 토큰 소유자(나)의 정보' - current token owner/my information), making the purpose evident. It doesn't explicitly differentiate from sibling tools like 'get_user_info', but the focus on 'current token owner' provides some implicit distinction.

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 through '현재 토큰 소유자(나)' (current token owner/me), suggesting this tool is for retrieving the authenticated user's own information rather than arbitrary user data. However, it doesn't explicitly state when to use this versus alternatives like 'get_user_info' or provide clear 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that results include the author's name and username automatically, which adds some context about output behavior. However, it lacks critical details such as whether this is a read-only operation, potential rate limits, authentication requirements, error handling, or pagination. For a tool with no annotations, this is a significant gap in transparency.

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 well-structured, consisting of two sentences that efficiently convey the tool's purpose and a key output feature. It is front-loaded with the main action and avoids unnecessary verbosity, 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 lack of annotations and output schema, the description is incomplete for effective tool use. It mentions output includes author details but doesn't cover other behavioral aspects like safety, performance, or error conditions. For a tool with no structured metadata, the description should provide more context to compensate, but it falls short.

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 does not explicitly mention the 'post_id' parameter or provide additional semantic context beyond what the input schema already covers. Since the schema description coverage is 100% (with a clear description for 'post_id'), the baseline score is 3. The description adds no extra parameter details, but it doesn't need to compensate for low 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 tool's purpose: '특정 게시물의 전체 스레드를 가져옵니다' (Get the entire thread of a specific post). It specifies the verb ('가져옵니다' - get/fetch) and resource ('게시물의 전체 스레드' - entire post thread), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_channel_messages' or 'search_messages', which might also retrieve message-related 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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, exclusions, or comparisons to sibling tools such as 'get_channel_messages' or 'search_messages', which could be used for similar purposes. Users are left to infer usage based on the tool name and description 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 behavioral aspects: whether this requires authentication, what format the team list returns (just names or full objects), whether it's paginated, if there are rate limits, or what happens when the user belongs to no teams. For a read operation 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 that states exactly what the tool does with zero wasted words. It's appropriately sized for a simple tool and front-loads the core functionality.

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 has no annotations, no output schema, and the description provides only basic functionality without behavioral context, this is incomplete. For a tool that fetches user data, the description should address authentication requirements, return format, error conditions, or limitations. The current description is too minimal for effective agent use.

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 0 parameters with 100% schema description coverage, so the schema already fully documents the lack of parameters. The description appropriately doesn't discuss parameters since none exist. It gets a baseline 4 for zero-parameter tools that don't need parameter explanation.

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 verb ('가져옵니다' - fetches/gets) and resource ('팀 목록' - team list) with scope ('현재 사용자가 속한' - that the current user belongs to). It distinguishes from siblings by focusing on teams rather than channels, messages, or users. However, it doesn't explicitly differentiate from potential similar team-related 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 provides no guidance on when to use this tool versus alternatives. It doesn't mention when this tool is appropriate versus searching for teams, getting team details, or using other sibling tools. There's no context about prerequisites, limitations, or alternatives.

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 the full burden of behavioral disclosure. It states this is a retrieval operation ('조회합니다'), implying it is read-only, but does not disclose other traits such as authentication requirements, rate limits, error conditions, or what happens if the user ID is invalid. The description adds minimal behavioral context beyond the implied read-only nature.

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 appropriately concise with two sentences: one stating the purpose and another listing example data fields ('username, 이름, 닉네임 등'). It is front-loaded with the core functionality, though the second sentence could be slightly more structured (e.g., clarifying if the list is exhaustive).

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 (single parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and hints at return data, but lacks details on behavioral traits, error handling, or differentiation from siblings, leaving gaps for an agent to operate effectively.

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 the 'user_id' parameter documented as '조회할 사용자의 ID' (ID of the user to retrieve). The description adds no additional meaning beyond this, such as format examples or constraints, 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 tool's purpose: '사용자 ID로 사용자의 상세 정보를 조회합니다' (Retrieve detailed user information by user ID). It specifies the verb '조회합니다' (retrieve) and resource '사용자의 상세 정보' (user's detailed information), but does not explicitly differentiate it from sibling tools like 'get_current_user' or 'search_users'.

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 does not mention sibling tools like 'get_current_user' (which might retrieve the current user without an ID) or 'search_users' (which might search by criteria other than ID), leaving the agent to infer 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that search results include author names and usernames, which adds some context about output behavior. However, it lacks details on permissions, rate limits, pagination, or error handling, which are important for a search operation. This partial disclosure earns a score of 2.

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, with two sentences that directly explain the tool's function and output. There is no unnecessary information, making it efficient. However, it could be slightly more structured by explicitly separating usage guidelines, which prevents a perfect score of 5.

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 search tool with no annotations and no output schema, the description is moderately complete. It covers the basic purpose and output inclusion but lacks details on result format, limitations, or error cases. This makes it adequate but with clear gaps, resulting in a score of 3.

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 input schema already fully documents the parameters. The description adds minimal value by mentioning keywords, usernames, and dates in the search, but does not provide additional syntax or format details beyond what the schema states. This meets the baseline of 3 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 tool's purpose: 'Mattermost에서 메시지를 검색합니다' (Search messages in Mattermost). It specifies the resource (messages) and the action (search), but does not explicitly differentiate it from sibling tools like 'search_user_messages' or 'get_channel_messages', which limits it to a 4 instead of a 5.

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 mentions search capabilities but does not compare to sibling tools like 'search_user_messages' or 'get_channel_messages', nor does it specify prerequisites or exclusions. This lack of comparative context results in a score of 2.

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 the full burden of behavioral disclosure. It states the tool searches messages, implying a read-only operation, but doesn't clarify aspects like permissions needed, rate limits, pagination, or what the output format might be. For a search tool with zero annotation coverage, this is a significant gap in transparency about how the tool behaves beyond its basic function.

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 brief and front-loaded, stating the core purpose in the first sentence. The second sentence adds useful examples without redundancy. It avoids unnecessary words, making it efficient, though it could be slightly more structured to highlight key points like the optional keyword parameter more explicitly.

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 (search functionality with 2 parameters), lack of annotations, and no output schema, the description is incomplete. It covers the basic purpose but fails to address behavioral aspects, usage context relative to siblings, or output expectations. This leaves significant gaps for an agent to understand how to effectively use the tool in practice.

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%, meaning the input schema already fully documents both parameters (user_name and keyword). The description adds minimal value by repeating the examples for user_name ('박찬우', 'cwpark') and mentioning keyword as optional, but doesn't provide additional semantics beyond what's in the schema. This meets the baseline of 3 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 tool's purpose: 'search messages of a specific user by name or username' (특정 사용자의 메시지를 이름이나 username으로 검색합니다). It specifies the verb (search) and resource (user messages), and provides concrete examples ('박찬우', 'cwpark'). However, it doesn't explicitly differentiate from sibling tools like 'search_messages' or 'search_users', which reduces clarity about its unique role.

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 minimal guidance on when to use this tool. It mentions that searches can be done by name or username with examples, but offers no advice on when to choose this over alternatives like 'search_messages' or 'search_users', nor does it specify prerequisites or exclusions. This lack of comparative context leaves the agent with insufficient direction.

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. It mentions the search functionality but doesn't disclose behavioral traits like whether it's read-only, what permissions are needed, if there are rate limits, pagination behavior, or what the return format looks like. For a search 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 without any wasted words. It's appropriately sized and front-loaded with the core functionality.

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 the tool returns, any behavioral constraints, or usage context. For a search tool, this leaves significant gaps for an AI agent to understand how to properly invoke and interpret results.

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 the parameter 'search_term' fully documented in the schema. The description adds minimal value by listing the searchable fields (name, username, nickname), but this is already implied by the schema's description. 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 ('search') and resource ('users') with specific search criteria ('by name, username, nickname'). It doesn't explicitly differentiate from sibling tools like 'search_messages' or 'search_user_messages', but the resource focus is clear.

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 like 'get_user_info' or 'get_current_user'. The description only states what it does, not when it's appropriate or what prerequisites might exist.

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