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 returns (authentication status and basic profile info) but doesn't mention error conditions, rate limits, or what happens when authentication fails. The description adds value by specifying the return content but lacks comprehensive 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 perfectly concise - two sentences that directly state what the tool does and what it returns. Every word earns its place with zero waste or redundancy. The information is front-loaded with the core purpose stated immediately.

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 authentication status check tool with no parameters and no output schema, the description is adequate but has gaps. It explains what information is returned but doesn't specify the format or structure of the response. Given the lack of annotations and output schema, more detail about the return format would improve completeness.

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 baseline is 4. The description appropriately doesn't waste space discussing non-existent parameters and focuses on the tool's function instead.

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 with specific verbs ('check', 'returns') and resources ('authentication status', 'Microsoft Graph connection', 'basic profile information'). It distinguishes itself from siblings like 'get_current_user' by focusing specifically on authentication status rather than general user data 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 clear context about when to use this tool - to check authentication status and get basic profile info. However, it doesn't explicitly state when NOT to use it or mention specific alternatives like 'get_current_user' for more detailed user information, which prevents a perfect score.

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 but offers minimal behavioral context. It mentions chat types (1:1/group) and optional topics, but doesn't disclose permissions needed, whether chats are private/public, if there are rate limits, what happens with duplicate user emails, or what the response looks like. For a creation 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 extremely concise with just two sentences that efficiently convey the core functionality. Every word earns its place - first sentence establishes the main action, second sentence provides essential context about chat types and optional features. No wasted words or redundancy.

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 creation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what gets returned (chat ID? success status?), doesn't mention error conditions, doesn't clarify whether userEmails must be valid/existing users, and provides no guidance on usage context relative to sibling tools. The conciseness comes at the expense of necessary completeness.

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 thoroughly. The description adds minimal value by mentioning 'topic (for group chats)' which slightly clarifies usage context, but doesn't provide additional semantics beyond what's in the schema descriptions. This 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 verb 'create' and resource 'new chat conversation', specifying it can be 1:1 or group chat. However, it doesn't explicitly differentiate from sibling tools like 'send_chat_message' which might also initiate chats, leaving some ambiguity about when to use this specific creation tool versus sending a first message.

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. With siblings like 'send_chat_message' that might also initiate conversations, there's no indication whether this is for empty chat creation versus starting with content, or any prerequisites like authentication status needed before creating chats.

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 the return content (replies with content, sender info, timestamps), which is helpful, but lacks critical details like pagination behavior, rate limits, authentication requirements, error conditions, or whether it's a read-only operation. For a tool with no annotations, this leaves significant gaps in understanding its 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 appropriately concise with two sentences: one stating the purpose and another detailing the return values. It's front-loaded with the core function, and every sentence adds value (the second clarifies output content). There's no wasted verbiage, though it could be slightly more structured with bullet points for returns.

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 for a tool with 4 parameters. It covers the basic purpose and return content but misses behavioral aspects (e.g., pagination, errors, auth), usage context relative to siblings, and deeper parameter insights. For a read operation in a collaborative toolset, this leaves too much unspecified.

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 fully documents all parameters. The description adds no additional parameter semantics beyond what's in the schema—it doesn't explain relationships between parameters (e.g., that teamId, channelId, and messageId form a hierarchy) or provide usage examples. This meets the baseline for high schema coverage but doesn't enhance understanding.

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 ('Get') and resource ('all replies to a specific message in a channel'), making the purpose immediately understandable. It distinguishes from siblings like 'get_channel_messages' by focusing on replies rather than primary messages. However, it doesn't explicitly contrast with 'reply_to_channel_message' or 'get_recent_messages', which slightly limits 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 prefer this over 'get_channel_messages' for replies, or how it relates to 'get_recent_messages' or 'search_messages'. Without such context, an agent might struggle to select the right tool for fetching message replies in different scenarios.

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. It mentions the tool retrieves 'recent messages' and specifies return fields, but lacks critical behavioral details: whether it's paginated, sorted (e.g., by timestamp), if it requires specific permissions, rate limits, or error conditions. For a read operation with no 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 that front-loads the core purpose and return values. Every word contributes meaning, though it could be slightly more structured (e.g., separating purpose from returns).

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 omits behavioral traits (e.g., pagination, sorting, permissions), doesn't clarify scope (e.g., how 'recent' is defined), and provides no usage guidance relative to siblings. For a 3-parameter tool in a rich sibling set, this leaves the agent under-informed.

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 clear parameter descriptions in the schema (e.g., 'Team ID', 'Channel ID', 'Number of messages to retrieve'). The description adds no parameter-specific information beyond what the schema provides, 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 ('Retrieve recent messages'), resource ('from a specific channel in a Microsoft Team'), and return values ('message content, sender information, and timestamps'). However, it doesn't explicitly differentiate from siblings like 'get_recent_messages' or 'get_chat_messages', which likely retrieve messages from different contexts.

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 siblings like 'get_recent_messages' (which might retrieve messages across channels) or 'get_chat_messages' (which might retrieve private chat messages), leaving the agent to guess based on 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 the full burden of behavioral disclosure. It mentions the return data but doesn't cover critical aspects like pagination behavior (e.g., how 'limit' interacts with date filters), error conditions (e.g., invalid chatId), rate limits, or authentication requirements. For a read operation with 7 parameters, 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?

The description is a single, well-structured sentence that efficiently conveys the core purpose and return data. It's front-loaded with the main action and avoids unnecessary details, 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 read tool with 7 parameters and no output schema, the description is minimally adequate. It covers the basic purpose and return format but lacks behavioral context (e.g., pagination, errors) and usage differentiation from siblings. The high schema coverage helps, but the absence of annotations and output schema means the description should do more to compensate.

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 fully documents all 7 parameters. The description adds no parameter-specific information beyond what's in the schema (e.g., it doesn't clarify interactions between 'since', 'until', and 'limit'). 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 verb ('retrieve') and resource ('recent messages from a specific chat conversation'), and specifies the return data ('message content, sender information, and timestamps'). However, it doesn't explicitly differentiate from siblings like 'get_recent_messages' or 'get_channel_messages', which likely have overlapping purposes.

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 'get_recent_messages' or 'get_channel_messages'. It doesn't mention prerequisites (e.g., needing a valid chatId) or exclusions, leaving the agent to infer usage from the tool name and parameters 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?

No annotations are provided, so the description carries full burden. It states this is a read operation ('Get') and specifies what information is returned, but doesn't disclose behavioral aspects like authentication requirements, rate limits, error conditions, or data freshness. The description adds value by listing returned fields but lacks 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, well-structured sentence that efficiently communicates the tool's purpose and key returned fields without any redundant information. It's front-loaded with the main action and resource.

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 provides basic purpose and output field examples but lacks details on authentication, errors, or full return structure. For a read-only tool with 0 parameters, this is adequate but leaves gaps in behavioral context that could help an agent use it correctly.

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 baseline is 4. The description appropriately doesn't discuss parameters since none exist, and it focuses on the tool's purpose and output instead.

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 specific verb 'Get' and resource 'current authenticated user's profile information', with explicit examples of included fields (display name, email, job title, department). It distinguishes from sibling tools like 'get_user' by specifying 'current authenticated' rather than a general user lookup.

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 (when you need profile info for the authenticated user) but doesn't explicitly state when not to use it or name alternatives. For instance, it doesn't contrast with 'get_user' for looking up other users or 'auth_status' for authentication state.

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 but only states what the tool does, not how it behaves. It doesn't disclose whether this is a read-only operation, what permissions are needed, rate limits, pagination behavior, or what the return format looks like. For a tool with 3 parameters and no output schema, 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?

Single sentence that efficiently communicates the core purpose with zero wasted words. Front-loaded with the main action and resource, making it immediately clear 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 tool with 3 parameters, 100% schema coverage, but no annotations and no output schema, the description is minimally adequate. It states what the tool does but lacks behavioral context about permissions, rate limits, and return format. The schema handles parameter documentation well, but the description doesn't compensate for the missing output schema and annotation information.

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 all parameters are documented in the schema. The description doesn't add any parameter-specific information beyond what's in the schema. The baseline of 3 is appropriate since the schema does the heavy lifting, though the description could have added context about how parameters interact.

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 ('Find') and resource ('all recent messages where the current user was mentioned') with specific scope ('across Teams channels and chats'). It distinguishes from siblings like get_recent_messages (general messages) and search_messages (broader search) by focusing exclusively on mentions of the current user.

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 for finding user mentions, but doesn't explicitly state when to use this tool versus alternatives like search_messages or search_users_for_mentions. No guidance on prerequisites or exclusions is provided, leaving the agent to infer context from 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 the full burden of behavioral disclosure. It states the tool retrieves messages with filtering but lacks critical details: whether it requires authentication, any rate limits, pagination behavior, error conditions, or the format of returned data. For a read operation with 10 parameters, 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 that front-loads the core purpose and summarizes filtering capabilities. It avoids redundancy and wastes no words, though it could be slightly more structured by separating purpose from filter examples.

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 (10 parameters, no annotations, no output schema), the description is incomplete. It doesn't address behavioral aspects like authentication needs, rate limits, or output format, which are crucial for a tool with extensive filtering options. The agent lacks sufficient context to use this tool 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 fully documents all 10 parameters. The description adds minimal value by listing filter types ('time range, scope, teams, channels, and users') but doesn't provide additional semantics beyond what's in the schema descriptions. 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 tool's purpose: 'Get recent messages from across Teams with advanced filtering options.' It specifies the verb ('Get'), resource ('recent messages'), and scope ('across Teams'), though it doesn't explicitly differentiate from siblings like 'get_channel_messages' or 'search_messages' beyond mentioning 'advanced filtering options.'

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 filtering options but doesn't compare to sibling tools like 'search_messages' or 'get_my_mentions,' leaving the agent to infer usage based on parameter 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?

No annotations are provided, so the description carries the full burden. It states this is a read operation ('Get') and describes the return data, but lacks details on permissions, error handling, rate limits, or whether it's idempotent. For a tool with zero annotation coverage, this is insufficient behavioral disclosure.

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 efficiently convey the tool's purpose and return data. However, it could be slightly more structured by separating usage guidelines or behavioral details, but it avoids unnecessary verbosity.

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 (1 parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and return values, but lacks completeness in usage guidelines and behavioral transparency, which are needed for effective agent operation.

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 parameter 'userId' documented as 'User ID or email address.' The description adds no additional parameter semantics beyond what the schema provides, such as format examples or validation rules, so it meets the baseline score of 3.

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: 'Get detailed information about a specific user by their ID or email address.' It specifies the verb ('Get'), resource ('user'), and scope ('detailed information'), but does not explicitly differentiate it from sibling tools like 'get_current_user' or 'search_users', which prevents a score of 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 like 'get_current_user' (for the current user) or 'search_users' (for broader queries). It mentions the input method (ID or email) but offers no context on appropriate use cases 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?

No annotations are provided, so the description carries the full burden. It states the tool lists channels and returns specific data, but doesn't disclose behavioral traits such as whether it's read-only, pagination handling, rate limits, authentication needs, or error conditions. The description is minimal and lacks 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 front-loads the purpose and key details without unnecessary words. It directly communicates what the tool does, the required input, and the output structure, 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 tool's low complexity (1 parameter, no output schema, no annotations), the description is adequate for basic understanding but incomplete. It covers the purpose and return data, but lacks details on behavioral aspects like safety, performance, or error handling, which are important for a tool with no annotations.

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 'teamId' documented as 'Team ID'. The description adds value by contextualizing this as 'a specific Microsoft Team', but doesn't provide additional semantics like format examples or validation rules beyond what the schema already covers.

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 ('List all channels') and resource ('in a specific Microsoft Team'), and specifies the return data ('channel names, descriptions, types, and IDs'). It distinguishes from siblings like 'list_teams' by focusing on channels within a team, but doesn't explicitly differentiate from other channel-related tools like 'get_channel_messages'.

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 by specifying 'in a specific Microsoft Team' and the required 'teamId' parameter, suggesting it's for retrieving channel information within a team context. However, it lacks explicit guidance on when to use this tool versus alternatives like 'list_teams' or 'get_channel_messages', and doesn't 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that the tool returns 'chat topics, types, and participant information,' which adds some context about output format. However, it doesn't cover important behavioral aspects like pagination, rate limits, sorting order, or whether 'recent' has a specific time window. For a list operation with zero annotation coverage, 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?

The description is concise and well-structured in two sentences. The first sentence clearly states the purpose, and the second adds useful context about return values. There's no wasted verbiage, and the information is front-loaded appropriately.

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 user chats) and the absence of both annotations and an output schema, the description is minimally adequate. It explains what the tool does and what information it returns, but lacks details on behavioral constraints, output structure, or differentiation from sibling tools. With no output schema, more detail about return values 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?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, earning a baseline score of 4 for not adding unnecessary information. It correctly focuses on the tool's purpose rather than non-existent 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 clearly states the tool's purpose: 'List all recent chats (1:1 conversations and group chats) that the current user participates in.' It specifies the verb ('List'), resource ('chats'), and scope ('recent', 'current user participates in'), but doesn't explicitly differentiate from sibling tools like 'list_channels' or 'get_chat_messages'.

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. For example, it doesn't mention how this differs from 'list_channels' (which lists channels, not chats) or 'get_chat_messages' (which retrieves messages within a specific chat). The description only states what the tool does, not when to choose it.

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 return fields (names, emails, roles, IDs), which is helpful, but doesn't cover critical aspects like pagination, rate limits, authentication requirements, or error conditions. For a list operation with zero annotation coverage, this leaves significant gaps in understanding tool 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 a single, efficient sentence that front-loads the core action ('List all members') and includes key return details. Every word earns its place with no redundancy or fluff, making it highly concise and well-structured.

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 (list operation with one parameter) and no output schema, the description is minimally adequate. It covers the purpose and return fields but lacks behavioral context (e.g., pagination) and usage guidelines. With no annotations to fill gaps, it's complete enough for basic use but leaves the agent guessing about finer details.

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% (teamId is fully described as 'Team ID'), so the baseline is 3. The description adds no additional parameter information beyond what the schema provides, such as format examples or sourcing details for teamId, but it doesn't need to compensate for gaps.

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 'List' and resource 'members of a specific Microsoft Team', making the purpose unambiguous. It distinguishes from siblings like list_channels or list_teams by specifying team members. However, it doesn't explicitly differentiate from search_users or get_user, which could also retrieve user information, 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?

The description provides no guidance on when to use this tool versus alternatives like search_users or get_user, which might retrieve similar user data. It doesn't mention prerequisites (e.g., needing team access) or exclusions, leaving the agent to infer usage from 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 the full burden. It discloses the return format (team names, descriptions, and IDs) and that it's a read operation, but lacks details on pagination, rate limits, authentication needs, or error conditions. It's adequate but minimal for 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?

Two sentences that are front-loaded with the core purpose and return values. Every word earns its place with no redundancy or fluff, making it highly efficient and 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?

Given the tool's low complexity (0 parameters, no output schema, no annotations), the description is complete enough for a basic list operation. It covers purpose and return format, though it could benefit from more behavioral details like pagination or error handling to be fully comprehensive.

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 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add param info, which is appropriate. Baseline is 4 for zero parameters, as it avoids unnecessary details.

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 'List' and resource 'Microsoft Teams', specifies scope 'that the current user is a member of', and distinguishes from siblings like list_channels and list_chats by focusing on teams. It's specific and unambiguous.

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 (current user's membership) but doesn't explicitly state when to use this tool versus alternatives like list_channels or list_team_members. No guidance on prerequisites or exclusions is provided, leaving usage somewhat open to interpretation.

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 behavioral traits like supporting text/markdown formatting, mentions, and importance levels, but lacks critical details such as permission requirements, rate limits, whether replies are editable/deletable, or how replies appear in the channel. For a mutation tool with 11 parameters, 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 front-loads the core purpose and lists key features. It avoids redundancy, but could be slightly more structured by separating purpose from capabilities 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 the tool's complexity (11 parameters, mutation operation) and lack of annotations or output schema, the description is incomplete. It does not cover error conditions, response format, or important behavioral aspects like side effects or authentication needs, leaving significant gaps for agent 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?

Schema description coverage is 100%, so the schema fully documents all 11 parameters. The description adds minimal value beyond the schema by hinting at features like formatting and mentions, but does not provide additional syntax, examples, 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 specific action ('Reply to a specific message in a channel') and resource ('message'), distinguishing it from siblings like 'send_channel_message' (which creates new messages) and 'get_channel_message_replies' (which retrieves replies). It specifies the reply targets a particular message via messageId.

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 mentioning 'reply to a specific message' and listing supported features, but it does not explicitly state when to use this tool versus alternatives like 'send_channel_message' (for new messages) or 'create_chat' (for private chats). No exclusions or prerequisites are 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the API and KQL syntax but lacks critical details like whether this is a read-only operation (implied by 'search'), potential rate limits, authentication requirements, or what the output format looks like (e.g., pagination, error handling). For a search tool with zero annotation coverage, this leaves significant gaps in understanding its 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 efficiently structured in two sentences: the first states the purpose and scope, and the second adds key capabilities. Every sentence earns its place by providing essential information without redundancy, making it front-loaded and appropriately sized for the tool's complexity.

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 (4 parameters, 100% schema coverage, no output schema, no annotations), the description is partially complete. It covers purpose and basic usage but lacks behavioral details (e.g., output format, error cases) and deeper contextual guidance. Without annotations or an output schema, the agent might struggle to fully understand how to interpret results or handle edge cases, leaving room for improvement.

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 each parameter well-documented in the schema (e.g., query supports KQL syntax, scope has enum values, limit has min/max). The description adds minimal value beyond the schema by mentioning 'advanced KQL syntax for filtering by sender, mentions, attachments, and more,' which slightly elaborates on the query parameter but doesn't provide new syntax or format details. 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 specific action ('Search for messages') and resource ('across all Microsoft Teams channels and chats'), distinguishing it from siblings like get_channel_messages or get_chat_messages that retrieve messages from specific sources without search capabilities. It also mentions the underlying API (Microsoft Search API) for technical context.

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 stating it searches 'across all Microsoft Teams channels and chats' and supports 'advanced KQL syntax for filtering,' suggesting it should be used for comprehensive, filtered searches rather than retrieving specific message sets like sibling tools. However, it doesn't explicitly state when not to use it or name alternatives, such as get_recent_messages for non-search retrieval.

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. It mentions the action ('Search') and return type ('basic profile information'), but lacks details on permissions required, rate limits, pagination behavior, or what constitutes 'basic profile information'. For a search tool with no 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 two concise sentences that are front-loaded with the core purpose and efficiently cover the search criteria and return value. There is no wasted language, 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?

Given the tool's moderate complexity (search function with one parameter) and no annotations or output schema, the description is adequate but incomplete. It covers the purpose and parameters but lacks behavioral details like error handling or output format specifics, which are important for 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?

The schema description coverage is 100%, with the parameter 'query' fully documented in the schema. The description adds minimal value by restating that the query is for 'name or email address', which is already implied in the schema. This meets the baseline of 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 tool's purpose with a specific verb ('Search') and resource ('users in the organization'), and distinguishes it from siblings by specifying the search criteria ('by name or email address') and what it returns ('basic profile information'). This differentiates it from tools like 'get_user' or 'list_team_members'.

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 searching for users by name or email, but does not explicitly state when to use this tool versus alternatives like 'get_user' (which likely retrieves a specific user) or 'list_team_members' (which might list all members without search). No exclusions or prerequisites are mentioned, leaving some ambiguity.

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 returns users with display names, email addresses, and mention IDs, which is useful. However, it doesn't mention important behaviors like whether it's a read-only operation (implied but not stated), if it requires authentication, rate limits, or how results are ordered. It adds some context but misses key operational details.

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 sentences, front-loaded with the core purpose and followed by return details. Every sentence earns its place by clarifying the tool's function and output without redundancy. It's appropriately sized and efficiently structured.

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 provides basic purpose and output format, but it's incomplete for a search tool. It lacks details on authentication needs, error handling, pagination (beyond the 'limit' param), or whether the search is case-sensitive. For a tool with 2 parameters and no structured safety hints, more context would be beneficial.

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 ('query' and 'limit'). The description does not add any meaning beyond what the schema provides—it doesn't explain parameter interactions, formatting nuances, or search logic. Baseline 3 is appropriate as 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 specific action ('Search for users to mention in messages') and resource ('users'), distinguishing it from sibling tools like 'search_users' (which likely has broader scope) and 'get_user' (which retrieves a specific user). It explicitly mentions the intended use case (mentioning in messages), making the purpose unambiguous.

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 for when to use this tool ('to mention in messages'), but it does not explicitly state when not to use it or name alternatives. For example, it doesn't clarify if 'search_users' should be used for non-mention searches or if 'list_team_members' is better for team-specific mentions. The guidance is helpful but lacks explicit 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. It mentions 'Supports text and markdown formatting, mentions, and importance levels' which describes capabilities but not behaviors. Critical behavioral aspects are missing: whether this is a mutating operation (implied but not stated), permission requirements, rate limits, error conditions, or what happens on success (e.g., returns message ID?). For a message-sending tool with 10 parameters, this is inadequate.

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 communicates the core purpose upfront. It wastes no words on redundancy or unnecessary elaboration. However, it could be more structured by separating purpose from capabilities for even clearer scanning.

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 complex mutation tool with 10 parameters, no annotations, and no output schema, the description is insufficient. It doesn't address critical context: mutation nature, authentication requirements, error handling, return values, or usage boundaries. The agent lacks enough information to use this tool confidently beyond basic parameter filling.

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 thoroughly. The description adds minimal value beyond the schema - it mentions 'text and markdown formatting' (covered by the 'format' enum), 'mentions' (covered by the 'mentions' array), and 'importance levels' (covered by the 'importance' enum). No additional semantic context is provided about parameter interactions or usage patterns.

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 ('Send a message') and target ('to a specific channel in a Microsoft Team'), which is specific and actionable. It distinguishes from siblings like 'send_chat_message' by specifying 'channel' rather than 'chat', but doesn't explicitly contrast them. The mention of supported features (formatting, mentions, importance) adds useful detail about capabilities.

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 'send_chat_message' or 'reply_to_channel_message'. It mentions supported features but doesn't specify prerequisites, constraints, or typical use cases. The agent must infer usage from the tool name and parameter 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions supported features (text/markdown formatting, mentions, importance levels) but lacks critical behavioral details: whether this requires specific permissions, if messages are editable/deletable after sending, rate limits, error conditions, or what happens on success. For a mutation tool with zero annotation coverage, this is inadequate.

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 and lists key features. There's no wasted text, but it could be slightly more structured by separating purpose from capabilities. It earns a 4 for being appropriately sized and clear.

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 (5 parameters, mutation operation) and lack of annotations or output schema, the description is incomplete. It doesn't address behavioral aspects like permissions, side effects, or response format, which are crucial for an AI agent to use it correctly. For a chat messaging tool with no structured safety or output info, this 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?

Schema description coverage is 100%, meaning all parameters are documented in the schema itself. The description adds minimal value beyond the schema by listing supported features (formatting, mentions, importance) that correspond to parameters, but doesn't provide additional semantic context like examples or constraints. With high schema coverage, the baseline is 3.

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: 'Send a message to a specific chat conversation.' It specifies the action (send) and resource (message to chat conversation), distinguishing it from sibling tools like 'send_channel_message' which targets channels. However, it doesn't explicitly contrast with 'reply_to_channel_message' or other messaging tools, keeping it at 4 rather than 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 doesn't mention when to choose 'send_chat_message' over 'send_channel_message' or 'reply_to_channel_message', nor does it specify prerequisites like needing an existing chat. Without any usage context or exclusions, this scores a 2.

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