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

With no annotations provided, the description must carry the full burden of behavioral disclosure. It adds value by specifying the ET timezone for start_time/end_time and by instructing the agent to review the returned time entry for what was actually added, hinting at possible modifications. However, it does not mention potential validation errors, permission requirements, or constraints like overlapping entries, leaving some behavioral aspects opaque.

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: a one-sentence purpose statement, two essential LLM Notes, and clear parameter/return documentation. Each section earns its place with no redundant or filler content.

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 create tool with an output schema, the description covers all necessary context: purpose, prerequisite steps (tag and description lookups), timezone expectations, parameter meanings, and a note to review the returned object. It is complete and actionable for the agent.

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 provides zero descriptions and only property names (tag, end_time, start_time, description). The description compensates fully by documenting each parameter with a human-readable explanation and a concrete example, including the timezone format. This adds substantial meaning beyond the raw schema.

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 begins with 'Logs a new time entry into Toggl,' which is a specific action (create) targeting a clear resource (time entry into Toggl). This clearly distinguishes it from sibling tools like get_tags and get_entry_descriptions, which are read-oriented.

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 LLM Notes explicitly instruct the agent to always call get_tags first and to check get_entry_descriptions for similar descriptions before using this tool. This provides concrete when-to-use guidance and names the alternative tools to consult first, going beyond generic context.

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

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

With no annotations provided, the description carries the full burden. It discloses a key behavioral trait—'Cached daily'—which informs the agent that results may not be real-time. However, it does not mention permissions, read-only nature, or output structure, leaving some behavioral aspects unspecified.

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, front-loaded sentence that conveys both the tool's purpose and an important caching behavior. Every word contributes value, with no redundancy or filler.

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 tool with no parameters and no output schema, the description is largely complete. It specifies what is returned (a list of existing time entry descriptions) and notes the caching behavior. However, it does not explicitly state the return format (e.g., array of strings), which could be further clarified given the absence of an output schema.

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 zero parameters, and the input schema is empty. Per the rubric, a zero-parameter tool receives a baseline of 4. The description adds no parameter details, which is appropriate since there are none to explain.

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 function: 'Get a list of existing time entry descriptions being used in Toggl.' It uses a specific verb (get), a specific resource (time entry descriptions), and context (in Toggl), distinguishing it from siblings like get_tags (tags) and create_time_entry (creation).

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 the tool is for retrieving existing time entry descriptions, but it does not explicitly state when to use it versus alternatives, nor does it mention any exclusions or prerequisites. Since there is no alternative guidance, the usage context is only implicitly clear from the purpose.

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?

Without annotations, the description carries the full burden. It discloses a key behavioral trait: 'Cached daily,' which informs users about potential staleness. However, it does not mention authentication, rate limits, or other side effects, so coverage is limited.

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 concise sentence with no unnecessary words. It efficiently conveys both the purpose and the caching behavior.

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 (no parameters) and the presence of an output schema, the description is largely complete. It covers the main purpose and caching, though it lacks usage guidance. Overall adequate for the complexity level.

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 zero parameters, so the baseline is 4. The description does not need to add parameter information, and the schema coverage is trivially 100%.

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 the resource 'tags' within the Toggl context. This distinguishes it from sibling tools like create_time_entry and get_entry_descriptions, which serve different functions.

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

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

No explicit guidance is provided on when to use this tool versus alternatives. The note 'Cached daily' implies data may be stale, but it does not explicitly state when to choose this tool over others.

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, the description carries the full burden of behavioral disclosure. It adds the caching behavior ('Cached daily') which is useful context for an agent. However, it does not explicitly confirm the operation is read-only or mention any other side effects, limitations, or error states.

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, with no filler or redundant information. It is appropriately sized for the tool's simplicity and front-loads the core purpose.

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 (no parameters) and the presence of an output schema (which description needn't replicate), the description is reasonably complete. It reveals caching behavior and the resource scope, though it could mention potential staleness implications more explicitly.

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 zero parameters, so the schema already fully covers all inputs. The description does not need to add parameter details, and the baseline for zero-parameter tools is a score of 4.

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

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

The description clearly states the tool retrieves a list of existing tags in Toggl, using a specific verb and resource. However, it does not differentiate from the sibling tool get_tags, which likely serves a similar purpose, so it cannot receive 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?

No guidance is provided on when to use this tool versus alternatives like get_tags. The description mentions 'Cached daily' but does not explicitly state that it is for when stale data is acceptable or that real-time queries should use another tool.

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