ai-budget-planner

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

With no annotations, the description carries full responsibility for behavioral disclosure. It merely states the action without mentioning side effects, idempotency, required permissions beyond an api_key, or what happens on duplicate names. This is a sparse disclosure for a mutation 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 a single, well-structured sentence that is front-loaded with the action and resource. No unnecessary words or repetition of schema properties.

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 five parameters, no annotations, and no output schema, the description is too minimal. It does not explain what the API returns, any prerequisites beyond the api_key, or the distinction between team and department, leaving the agent with incomplete operational 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 100%, so the schema already documents all five parameters. The description adds no parameter-specific detail beyond the phrase 'monthly LLM budget allocation,' which loosely echoes monthly_limit_usd. It does not compensate for or add nuance to the 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 clearly states the action (create) and the resource (monthly LLM budget allocation) with a target audience (team or department). It distinguishes itself from sibling tools like get_budget and department_report, which are about retrieval or 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 when to use the tool (when creating a budget allocation), but it does not explicitly discuss alternatives or exclusions. No guidance on when to choose this over department_report or weekly_report, though the verb 'create' gives some contextual distinction.

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 what data is returned but does not explicitly confirm the operation is read-only, mention authentication requirements (despite an api_key parameter), or describe error/rate-limit behavior. The 'Get' verb implies read-only but is not explicit.

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 that is front-loaded with the action and resource, conveying the essential purpose without any unnecessary words. It is 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?

For a simple tool with two optional parameters and no output schema, the description adequately covers the main output content (spend, utilisation, alert status). It could be more complete by noting the read-only nature or authentication, but for this complexity level, it is reasonably 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 schema covers 100% of the parameters with clear descriptions (api_key and department), so the baseline is 3. The description's 'per department' aligns with the department filter but adds no new semantic information 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 uses the specific verb 'Get' and clearly identifies the resource as a department report with month-to-date spend, utilisation, and alert status. It is unambiguous but does not explicitly differentiate itself from sibling tools like weekly_report.

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 such as weekly_report or get_budget_alerts. There is no mention of use cases, prerequisites, or exclusions, leaving the agent without decision 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, the description carries the full burden. It implies a read-only operation via 'Get,' but provides no context about error behavior, permissions, or rate limits. It does disclose the core information returned (utilisation, spend breakdown, alert status), which is adequate for a simple getter, but lacks deeper 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, well-structured sentence that concisely lists the three key data points returned. It is appropriately sized and front-loaded with the verb 'Get,' making it 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?

Without an output schema, the description does a good job of explaining the return content (utilisation, spend breakdown, alert status). It is not overly verbose and covers the essential information for a budget getter. However, it lacks a note about what the tool does NOT do (e.g., historical reports), which would enhance completeness given the sibling tools.

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 descriptions for both parameters (api_key and budget_id). The description adds no additional parameter semantics beyond what the schema already provides, so it meets the baseline for high 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 function: 'Get current utilisation, spend breakdown by provider, and alert status for a budget.' It names the resource (budget) and the specific data returned. However, it does not explicitly distinguish itself from sibling tools like get_budget_alerts or department_report, so it lacks the sibling differentiation required for 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?

There is no guidance on when to use this tool versus alternatives. The description only states what it does, without prerequisites, exclusions, or mention of when get_budget_alerts would be more appropriate. This qualifies as '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, the description carries the full burden. It states the action ('List') and the nature of data (threshold alerts triggered), implying a read-only operation. However, it does not disclose whether alerts are historical or only currently active, how results are ordered, or any permission requirements. The threshold information adds context but the behavior is not fully transparent.

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, focused sentence that front-loads the action ('List all') and includes the specific threshold values. Every part is useful and there is no redundant or vague wording.

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 list tool with two parameters and no output schema, the description covers the core purpose and thresholds. However, it lacks details about the response format, ordering, or whether the alerts are active/historical. Given no output schema, the description could provide more context on what a returned alert looks like, making this merely adequate.

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 (api_key and budget_id). The description adds no additional meaning beyond the schema; it only reinforces that alerts are for a budget. Baseline 3 is appropriate as the description does not need to compensate for missing schema 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 uses the verb 'List' with a specific resource ('threshold alerts triggered for a budget') and includes the exact threshold values (50%, 75%, 90%, 100%). This clearly distinguishes it from sibling tools like get_budget or department_report by focusing on alert notifications.

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 when to use the tool (when you need to see budget threshold alerts), but it does not explicitly state alternatives or exclusion criteria. For example, it doesn't mention that get_budget covers general budget details or that weekly_report might summarize alerts. The context is clear but not 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?

With no annotations, the description carries the full burden. It states the core behavior (listing providers, models, and cost estimates) and adds detail about 'blended cost estimates' beyond the tool name. However, it omits any mention of edge cases, data source, or potential limitations, making it adequate but not exceptionally transparent.

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 with no filler. Every word earns its place, conveying the tool's purpose and output in a concise manner.

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, no output schema, no annotations), the description is complete. It clearly states what the tool lists, making it sufficient for an agent to understand the tool's behavior and expected output.

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 baseline for such tools is 4. The description does not need to explain parameter semantics, and it goes beyond baseline by describing what the user will get (providers, models, cost estimates), which adds value.

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 a specific verb 'List' and clearly states the resource: supported LLM providers, their models, and blended cost estimates per 1M tokens. This distinguishes it from sibling budget tools, which focus on managing budgets rather than enumerating providers.

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 explicit guidance on when to use this tool versus alternatives, nor does it mention any prerequisites or exclusions. While the tool's purpose is clear from context, the description itself offers no usage direction.

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 indicates a read operation through the verb 'Get' and describes the output as a digest broken down by department and provider, but it does not explicitly mention side-effects, authentication specifics, or any additional behavioral nuances. This is adequate but not rich.

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 is front-loaded with the primary action and outcome. Every word contributes value, and there is no unnecessary verbosity or repetition.

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 output schema, all parameters optional, no nested objects), the description provides a complete enough picture: it specifies the time window (7-day), the content (LLM spend digest), and the breakdown dimensions. It does not detail the exact response format, but for this straightforward read tool, the description is sufficient.

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 100% coverage for all three parameters (api_key, provider, department) with clear descriptions. The tool description adds context by mentioning 'broken down by department and provider', which helps map the filters to the digest breakdown, but this is minimal beyond what the schema already conveys. Baseline of 3 is appropriate.

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: to 'Get a 7-day LLM spend digest broken down by department and provider'. It uses a specific verb ('Get') and identifies both the resource ('LLM spend digest') and the breakdown dimensions, effectively distinguishing it from sibling tools like department_report which likely focuses on a single department.

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 a clear context for use: when a weekly (7-day) digest of LLM spend is needed. While it does not explicitly name alternatives or exclusion criteria, the context is sufficiently clear given the sibling tool names, and the optional filters suggest flexible usage for provider or department-specific views.

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