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

No annotations are provided, so the description must reveal behavioral traits. It only states the action and parameter context, but does not disclose potential side effects, constraints (e.g., duplicate names), error conditions, or whether the operation is reversible. This is a mutating tool, and the description offers no warning or extra behavioral detail.

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 that front-loads the main action and key context. There is no wasted information or redundancy, 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?

The tool is simple with only two parameters and no nested objects, and an output schema exists, so the description need not explain return values. However, as a mutation tool with no annotations, it is missing useful context such as preconditions or side effects. For a simple add operation, the description is minimally viable but not thorough.

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 provides parameter names and types but no descriptions. The description adds the word 'initial' to base salary, clarifying that it's the starting salary, but it does not explain the 'name' parameter. Since schema_description_coverage is 0%, the description partially compensates for one parameter but not fully.

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 ('Adds') and resource ('a new person to the budget database'), and also mentions the initial base salary. This distinguishes it from sibling tools like log_expense and initialize_new_month, which deal with different entities/actions.

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 clear usage: it is the tool for adding a person to the budget database. While it doesn't explicitly compare with alternatives or state when not to use it, the context of the sibling tools makes the intended use obvious. It gives clear context without excluding any specific 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 must carry the burden. It clearly states that the tool returns data, implying a read-only operation, but it does not disclose prerequisites (e.g., month initialization) or potential side effects. The description adds context about the returned content but lacks behavioral depth.

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 sentence, front-loaded with the verb and key output details. It is concise and free of fluff, making it easy for an agent 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?

The tool is relatively simple, but the description lacks parameter explanations and prerequisites. An output schema exists, which likely covers the return format, but the description alone is not fully sufficient for confident invocation of the 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?

Schema description coverage is 0%, and the description does not explain the 'name' or 'month' parameters. While the parameter names are somewhat self-explanatory, no format, examples, or field meaning are provided. The description fails to compensate for the missing schema 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 uses the specific verb 'Returns' and clearly identifies the resource (monthly statement) and its content (full breakdown of logged expenses and remaining budget balance). This distinguishes it from sibling tools like add_person and log_expense by focusing on read-only reporting.

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 use for retrieving monthly expense summaries but does not explicitly state when to use it versus alternatives, nor does it mention any exclusions like 'must initialize the month first'. The guidance is implied rather than explicit.

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 must fully disclose side effects and permissions. It only mentions the prompting behavior for salary updates but does not state whether this is a write operation, what happens to existing data, or any prerequisites. This is a significant gap for a mutating 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 concise with two compact sentences that avoid filler and redundancy. It directly states the primary action and one salient behavior. While more detail would be useful, the structure is clean and efficient.

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?

Although an output schema exists, the description lacks critical context about the tool's effects, parameter formats, and relationship to sibling tools. With no annotations and multiple parameters, the description is insufficient for an agent to invoke the tool correctly, especially regarding the salary-related parameters and side effects.

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 0%, so the description must compensate. It only hints at parameters (e.g., 'for a person' pointing to 'name', 'this new month' pointing to 'month') and implicitly references 'has_salary_changed' via 'salary has updated'. It does not explain the month format, the required boolean, or the meaning of 'new_salary_amount'.

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?

Uses a specific verb 'Initiates' with a clear resource 'a new tracking month for a person'. This distinguishes it from siblings like add_person or log_expense by its unique focus on month initialization, though it doesn't explicitly name alternatives.

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 this tool is used at the start of a new month for a person, but it does not explicitly say when to use it versus alternatives or any exclusions. The mention of prompting for salary changes gives some contextual clue but no explicit 'when-to-use' 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?

The description discloses key side effects: adding to the month's total, deducting from salary, and computing the remaining balance. With no annotations provided, this is essential and well-communicated, though it omits potential error conditions or authentication needs.

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 front-load the core action and effects. Every sentence adds value with no redundancy, achieving high information density.

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?

The description covers the basic operation but lacks context on prerequisites (e.g., an initialized month or existing salary) and failure modes. Given the sibling tools suggesting a workflow, this omission could lead to incorrect usage. An output schema exists, but it does not negate the need for usage context.

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

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

Schema description coverage is 0%, so the description must compensate. It implies 'amount_in_dollars' represents the expense amount, but it does not clarify the meaning of 'name', 'month', or 'description'. The mapping between parameters and concepts is incomplete, offering only minimal value beyond the schema's bare names.

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 (adds a new expense) and its consequences (deducts from salary, computes remaining balance). It distinguishes itself from siblings like 'add_person' and 'initialize_new_month' by specifying the resource (monthly expense).

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 usage context is implied: it is for logging expenses. However, there is no explicit guidance on when to use it versus alternatives or mention of prerequisites like having an initialized month or salary set. This leaves the agent without clear when/when-not guidance.

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