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 key behavioral traits: it retrieves activities, applies automatic summation by date/project/task, and allows optional project filtering. However, it doesn't cover aspects like pagination, rate limits, authentication needs, or error handling, which are important for a read operation with potential large datasets.

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 sized with two sentences: the first states the core purpose and key feature (automatic summation), and the second adds optional filtering. It's front-loaded with essential information and avoids redundancy, though it could be slightly more structured (e.g., clarifying the summation output).

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 (read operation with summation and filtering), no annotations, and no output schema, the description is moderately complete. It covers the main action and parameters but lacks details on output format (e.g., how summation is presented), error cases, or performance considerations, which would help an agent 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 fully documents the parameters (startDate, endDate, projectId). The description adds minimal value beyond the schema by mentioning 'date range' and 'filter by project ID,' but doesn't provide additional semantics like format nuances or usage tips. 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 tool's purpose: 'Get all activities within a date range with automatic summation by date, project, and task.' It specifies the verb ('Get'), resource ('activities'), and key behavior ('automatic summation'). However, it doesn't explicitly differentiate from sibling tools like 'get_user_projects' or 'get_user_project_tasks', which appear related but serve different 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 implies usage by mentioning 'within a date range' and 'Optionally filter by project ID,' suggesting it's for retrieving summarized activity data. However, it lacks explicit guidance on when to use this tool versus alternatives (e.g., vs. 'get_user_projects' for project details) or any exclusions (e.g., not for real-time data). This leaves some ambiguity in 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 full burden. It mentions output details ('daily breakdown and total calculations'), which adds some behavioral context beyond basic retrieval. However, it lacks critical information like whether this is a read-only operation, potential rate limits, authentication requirements, or data source specifics, leaving significant gaps for a tool with no annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose ('Get all public holidays for a specific year') and adds valuable output details without redundancy. Every word earns its place, making it appropriately sized and well-structured for quick comprehension.

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 nested objects) and high schema coverage, the description is minimally adequate. However, with no annotations and no output schema, it should provide more behavioral context (e.g., read-only nature, response format). The mention of 'daily breakdown and total calculations' helps but doesn't fully compensate for the lack of structured data.

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 'year' parameter fully documented in the schema (type, range, example). The description adds no additional parameter semantics beyond implying the year is used for retrieval. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't detract either.

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 ('public holidays'), specifying the scope ('for a specific year') and output details ('daily breakdown and total calculations'). It distinguishes from sibling tools like 'get_user_holidays' by focusing on public rather than user-specific holidays. However, it doesn't explicitly contrast with all siblings, 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. It doesn't mention prerequisites, exclusions, or compare it to sibling tools like 'get_user_holidays' for personal vs. public data. Without such context, an agent must infer usage from the tool name 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 mentions 'utilization calculations and remaining vacation days' which adds some context about what the tool returns, but doesn't cover important aspects like whether this requires authentication, rate limits, pagination behavior, or error conditions. For a tool that presumably accesses user data, 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 communicates the core purpose without any wasted words. It's appropriately sized for a simple single-parameter 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 simple read operation with 100% schema coverage but no annotations and no output schema, the description is adequate but incomplete. It explains what the tool returns ('utilization calculations and remaining vacation days') which partially compensates for the missing output schema, but doesn't provide enough behavioral context for a tool that likely requires authentication and returns potentially sensitive user data.

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 'year' well-documented in the schema. The description doesn't add any parameter-specific information beyond what's already in the schema. This meets the baseline of 3 when schema coverage is high and no additional parameter context is provided.

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 ('Get all user holidays'), resource ('user holidays'), and scope ('for a specific year with utilization calculations and remaining vacation days'). It distinguishes from siblings like get_public_holidays (which likely lacks user-specific calculations) and get_user_sick_days (different type of time off).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, when not to use it, or how it differs from sibling tools beyond implicit scope differences. The agent must infer usage from tool names alone.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'daily aggregation and total calculations,' which adds some context about output behavior, but fails to address critical aspects like whether this is a read-only operation, potential rate limits, authentication requirements, or error conditions. For a data retrieval tool, this leaves significant gaps in understanding how it behaves.

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. It avoids redundancy and wastes no words, though it could be slightly more structured by separating purpose from behavioral details 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 moderate complexity (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and hints at aggregation behavior, but lacks details on output format, error handling, or integration with sibling tools, leaving room for improvement in 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 input schema fully documents both parameters (startDate and endDate) with their formats. The description adds no additional parameter semantics beyond what's in the schema, such as date range constraints or handling of invalid dates, meeting 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 tool's purpose: 'Get user presences within a date range with daily aggregation and total calculations.' It specifies the verb ('Get'), resource ('user presences'), and scope ('date range'), but doesn't explicitly differentiate from sibling tools like get_user_holidays or get_user_sick_days, which might also retrieve user-related data within date ranges.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, exclusions, or compare it to sibling tools such as get_user_holidays or get_user_sick_days, leaving the agent to infer usage 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 search functionality and case-insensitive behavior (implied from the schema), but lacks details on permissions, rate limits, pagination, or error handling. For a tool that likely interacts with user data, this is a significant gap in transparency, as it doesn't address potential constraints or side effects beyond basic operation.

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 highly concise and well-structured, consisting of just two sentences that efficiently convey the tool's functionality and conditional behavior. Every sentence earns its place by providing essential information without redundancy, making it easy to parse and understand quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (one optional parameter, no output schema, no annotations), the description is adequate but incomplete. It explains what the tool does and how the parameter affects output, but lacks details on return format, error cases, or integration with sibling tools. For a user-facing query tool, more context on results and limitations would enhance completeness, though it meets minimum viability.

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 'query' parameter well-documented in the schema itself. The description adds minimal value beyond the schema by reiterating the optional nature of the query and its effect on output. Since the schema already covers the parameter semantics comprehensively, the baseline score of 3 is appropriate, as the description doesn't provide additional meaningful context.

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 all projects assigned to the current user or search within assigned projects by name/description.' It specifies the verb ('Get'), resource ('projects'), and scope ('assigned to the current user'), making it easy to understand. However, it doesn't explicitly differentiate from sibling tools like 'get_user_project_tasks', which focuses on tasks rather than projects.

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 some usage guidance by explaining the conditional behavior: 'If no query is provided, returns all assigned projects.' This implies that the tool can be used for both listing all projects and searching. However, it doesn't offer explicit when-to-use guidance compared to alternatives (e.g., when to use this vs. 'get_user_project_tasks' or other sibling tools), leaving the context somewhat implied rather than clearly defined.

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 adds context about the restriction to 'projects assigned to the current user,' which is useful beyond the input schema. However, it lacks details on other behavioral traits such as response format, pagination, error handling, or rate limits, leaving gaps for a mutation-free but context-sensitive tool.

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 front-loaded with the core purpose in the first sentence and adds a crucial restriction in the second. Both sentences earn their place by providing essential information without redundancy or fluff, making it highly efficient 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 low complexity (1 parameter, no output schema, no annotations), the description is mostly complete. It covers the purpose and key usage restriction. However, it lacks output details (e.g., what 'tasks' include, format) and could mention error cases (e.g., invalid project ID), leaving minor gaps for full contextual understanding.

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 'projectId' fully documented in the schema. The description does not add any additional meaning or syntax details beyond what the schema provides (e.g., it doesn't explain what constitutes a valid project ID beyond the schema's 'exclusiveMinimum: 0'). Baseline 3 is appropriate as the schema handles the parameter documentation.

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 ('Get all tasks') and resource ('for a specific assigned project by project ID'), distinguishing it from sibling tools like 'get_user_projects' (which retrieves projects, not tasks) and 'get_activities' (which retrieves activities, not tasks). It precisely defines the tool's function without redundancy.

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 explicit context for when to use this tool: 'Only works for projects assigned to the current user.' This clarifies the prerequisite condition. However, it does not explicitly state when not to use it or name alternatives (e.g., if tasks for unassigned projects are needed), 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves data with calculations, implying a read-only operation, but doesn't clarify permissions, rate limits, data freshness, or error conditions. For a tool with no annotation coverage, this leaves significant behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose and includes key details without waste. Every part of the sentence earns its place by specifying the action, resource, scope, and output characteristics.

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 (retrieving and calculating sick days), lack of annotations, and no output schema, the description is adequate but incomplete. It covers the purpose and output characteristics but misses behavioral details like permissions or error handling. It's minimally viable for a read operation but could be more 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 100% description coverage, with the 'year' parameter fully documented. The description adds context by specifying 'for a specific year' and mentioning 'daily breakdown and total calculations', which provides additional meaning about the output structure. However, it doesn't add syntax or format details beyond what the schema already provides.

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 ('Get') and resource ('user sick days'), including scope details ('for a specific year with daily breakdown and total calculations'). It distinguishes from siblings by focusing on sick days rather than activities, holidays, presences, or projects. However, it doesn't explicitly differentiate from similar tools like 'get_user_holidays' beyond the resource name.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, exclusions, or comparisons with sibling tools like 'get_user_holidays' or 'get_user_presences', leaving the agent to infer usage context solely from the tool name and description.

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