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 uses natural language but doesn't explain how it processes queries (e.g., parsing accuracy, supported formats), what the output looks like (e.g., report format, data included), or any limitations (e.g., query complexity, error handling). This leaves significant gaps in understanding the tool's behavior, scoring a 2 for inadequate 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 extremely concise and front-loaded with a single sentence: 'Get time reports using natural language.' It efficiently conveys the core purpose without unnecessary details, earning a 5 for zero waste and appropriate sizing given the tool's simplicity.

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 (natural language processing for reports) and lack of annotations or output schema, the description is incomplete. It doesn't address how reports are generated, what data they include, or any behavioral traits like error handling. For a tool that likely involves nuanced processing, this minimal description fails to provide sufficient context, scoring a 2.

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 'text' parameter well-documented as a natural language query including examples. The description adds minimal value beyond the schema, only reiterating the natural language aspect without providing additional syntax or format details. This meets the baseline of 3 for high schema coverage, but doesn't enhance parameter understanding further.

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 time reports using natural language.' It specifies the verb ('Get') and resource ('time reports'), and distinguishes it from siblings like 'list_entries' or 'log_time' by focusing on report generation rather than listing or logging. However, it doesn't explicitly differentiate from all siblings (e.g., 'list_projects' might also involve time-related data), keeping it at a 4 rather than 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 doesn't mention scenarios where natural language queries are preferred over structured queries (e.g., from siblings like 'list_entries'), nor does it specify prerequisites or exclusions. This lack of usage context leaves the agent with minimal direction, scoring a 2 for no guidance.

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 a read operation ('List') but lacks details on permissions, pagination, rate limits, or what 'recent' entails. This is inadequate for a tool with potential behavioral nuances.

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 with zero waste. It's appropriately sized and front-loaded, making it easy to scan and understand the core purpose 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?

Given no annotations, no output schema, and a simple input schema, the description is incomplete. It doesn't address behavioral aspects like return format, error handling, or how it differs from siblings, leaving gaps for an AI agent to understand full context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, with clear descriptions for 'from' and 'to' parameters. The description adds no additional meaning beyond the schema, as it doesn't explain how 'recent' relates to the date range or provide usage examples. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'List recent time entries' states a clear verb ('List') and resource ('time entries'), but lacks specificity about scope or differentiation from siblings like 'get_time_report' or 'log_time'. It's vague about what 'recent' means compared to the date parameters in the schema.

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 such as 'get_time_report' (which might offer more detailed reporting) or 'log_time' (for creating entries). The description implies a basic listing function but offers no explicit context 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 full burden. It states it's a list operation, implying read-only behavior, but doesn't disclose any behavioral traits like pagination, sorting, filtering capabilities, rate limits, or authentication requirements. For a tool with zero annotation coverage, this is a significant gap.

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

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

The description is a single, efficient sentence that directly states the tool's purpose without any wasted words. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly.

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

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

Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate but lacks completeness. It doesn't explain what 'available' means (e.g., active vs. archived), the return format, or any constraints, leaving gaps for the agent to handle.

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 inputs. The description adds no parameter information, but with no parameters, a baseline of 4 is appropriate as there's nothing to compensate for.

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 the resource 'available Harvest projects', making the purpose immediately understandable. It doesn't differentiate from siblings like 'list_entries' or 'list_tasks', which would require specifying what distinguishes projects from other listable resources.

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_time_report' or 'list_entries'. The description implies it's for listing projects but doesn't specify context, prerequisites, or exclusions, leaving the agent to infer usage from the 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?

No annotations are provided, so the description carries the full burden. It states it 'lists' tasks, implying a read-only operation, but doesn't disclose behavioral traits such as pagination, sorting, filtering beyond project_id, authentication needs, rate limits, or what happens if the project_id is invalid. For a tool with zero annotation coverage, 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 a single, efficient sentence with zero waste. It's front-loaded with the core purpose and appropriately sized for a simple tool. Every word earns its place without 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?

Given no annotations, no output schema, and a simple input schema, the description is incomplete. It doesn't explain return values (e.g., task details, format), error handling, or behavioral context. For a tool that likely returns a list of tasks, more information on output and usage would be helpful to 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 parameter 'project_id' documented as 'Project ID'. The description adds minimal value beyond the schema, only implying that tasks are listed for a project. Baseline is 3 since the schema does the heavy lifting, but the description doesn't compensate with additional context like format or constraints.

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 ('tasks'), specifying it's for a project. It distinguishes from siblings like 'list_projects' (which lists projects) and 'list_entries' (which likely lists time entries). However, it doesn't specify scope (e.g., 'all' or 'active') or differentiate from 'get_time_report' (which might involve tasks).

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

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

No guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a valid project_id) or exclusions (e.g., not for archived tasks). The context implies it's for listing tasks, but there's no explicit comparison to siblings like 'log_time' (which might create tasks).

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 logs time but doesn't disclose behavioral traits such as whether this is a write operation (implied but not confirmed), if it requires authentication, what happens on success/failure, or any rate limits. This leaves significant gaps in understanding the tool's behavior.

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

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

The description is a single, efficient sentence with zero waste. It's front-loaded with the core purpose and method, making it easy to parse quickly. Every word earns its place without redundancy or unnecessary elaboration.

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

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

Given the tool's complexity as a write operation with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns, error conditions, or integration context, leaving the agent with insufficient information for reliable use. This is inadequate for a mutation tool without structured support.

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 'text' fully documented in the schema. The description adds minimal value by reiterating 'natural language' but doesn't provide additional semantics beyond what the schema already specifies (e.g., examples or formatting nuances). Baseline 3 is appropriate as the schema handles 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 ('Log time entry') and the method ('using natural language'), which distinguishes it from sibling tools like 'get_time_report' or 'list_entries' that likely retrieve rather than create data. However, it doesn't specify the resource being logged (e.g., to a timesheet system), making it slightly less specific than a perfect score.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'list_entries' for viewing logs or 'list_projects' for context. It implies usage for logging time but doesn't mention prerequisites, exclusions, or explicit alternatives, leaving the agent to infer 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.