ai-cost-optimizer

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

With no annotations, the description carries the transparency burden. It does disclose a key behavior (raising a flag when a threshold is reached), but it does not mention side effects of the 'set' action (e.g., persistence, overrides, permission requirements) or what 'check' returns. Some behavior is disclosed, but not comprehensive.

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 sentences, front-loaded with the primary purpose and a concise behavioral note. Every word earns its place, with no fluff or 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?

For a 4-parameter tool without an output schema, the description does not explain what the 'check' action returns (e.g., current status object) or what the flagging entails (e.g., alert severity, notification). It gives the general idea but lacks return-value details that would help an agent interpret results.

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 explains all four parameters. The description adds little beyond this, merely describing the two main actions ('set' and 'check') which are already encoded in the action enum. Baseline of 3 is appropriate since the description provides no additional parameter-level insight.

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 specific verbs 'set' and 'check' with a clear resource ('budget limits for teams'). It also describes the core behavior (raising a flag on threshold), which distinguishes it from sibling tools like cost_forecast and cost_tracker that focus on prediction or tracking rather than limit configuration.

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 action parameter (set/check) implies when to use each mode, but the description does not explicitly state when to choose this tool over alternatives like cost_tracker or cost_forecast. There is no mention of scenarios where another tool would be preferred or excluded, so usage guidance is only implied.

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 burden of behavioral disclosure. It adds context by revealing the projection method ('daily-average linear projection'), which is useful, but it does not explicitly state whether the operation is read-only or if there are side effects, permissions, or limitations. It also does not describe the output format.

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, coherent sentence that front-loads the core purpose and method. It contains no filler or redundant information, making it appropriately concise.

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 three well-documented parameters and no output schema, the description sufficiently explains the purpose and methodology. However, it lacks explicit guidance on when to use it versus siblings, and does not mention output expectations or edge cases (e.g., insufficient historical data). This leaves some gaps for an agent relying solely on the description.

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 already provides descriptions for all three parameters (100% coverage), so the description does not need to add parameter details. The description does not add extra meaning beyond what the schema offers, maintaining the baseline score of 3.

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 a specific verb ('Forecast'), a resource ('future AI spend'), and a method ('daily-average linear projection'). It is readily distinguishable from sibling tools like budget_alert, cost_tracker, and model_breakdown.

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 a forecast of AI spend is needed based on historical usage), but it does not provide explicit exclusions or reference alternatives such as 'use budget_alert for budget monitoring'. The guidance is implied rather than stated.

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 of behavioral disclosure. It mentions persistence to Cloudflare KV, which is useful, but it does not disclose the side effects of the 'record' action (e.g., cost calculation logic, overwrite behavior) or what 'get' returns, including any authentication or rate-limit considerations.

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, front-loaded with the primary purpose and followed by a valuable persistence note. Every word contributes, with no 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?

The tool has dual actions ('record' and 'get') and six parameters, with no output schema or annotations. The description covers the core purpose but omits action-specific behavior, response format, and guidance on which parameters are needed per action. Given the presence of siblings, more contextual detail would improve 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 baseline is 3. The description adds 'by team' which maps to the team parameter, but it does not elaborate on parameter formats, defaults, or relationships 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 function: 'Record and retrieve AI API call costs by team.' This is a specific verb+resource combo that distinguishes it from sibling tools like cost_forecast (forecasting) and model_breakdown (breakdown analysis).

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 for recording and retrieving costs, but does not explicitly state when to choose this tool over alternatives. No exclusions or conditions are mentioned, and sibling tools like budget_alert or cost_forecast are not referenced.

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 must carry the full burden. It indicates a read-only 'analyze' operation and the grouping by model, but it omits details such as whether special permissions are required, whether data is real-time or aggregated, or how edge cases (e.g., no data) are handled.

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 redundancy. It efficiently communicates the purpose without extra words.

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?

There is no output schema, so the description should explain return values. It mentions 'broken down by model' which implies the response will include model names and costs, but it does not specify fields like currency, cost amounts, or whether it returns a time series. Adequate for a simple tool but incomplete for precise usage.

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 descriptions for both parameters with 100% coverage. The description reinforces the team and time-window aspects but adds no additional meaning beyond what the schema already specifies.

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 is specific and clear: 'Analyze costs broken down by model' identifies the verb (analyze), resource (costs), and grouping dimension (model). It distinguishes itself from sibling tools like budget_alert or cost_forecast by focusing on per-model cost breakdown.

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 per-model cost analysis over a time window, but it does not explicitly state when to use this tool versus alternatives like cost_forecast (future predictions) or cost_tracker (general tracking). No exclusions or alternative tool references are provided.

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 burden. It does mention 'Stateless — no KV required,' which is a useful behavioral disclosure about no side effects, but it doesn't explicitly say the operation is read-only, whether it requires authentication, or what the return format is. 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 extremely concise: two short sentences. The first sentence states the purpose, the second adds a valuable behavioral note. There is zero redundancy, and it is front-loaded with the action.

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 calculator with only three parameters and no output schema, the description is mostly complete. It clearly states what it does and the stateless nature. However, it does not explicitly describe the return value (e.g., a numeric cost in a specific currency), which would be helpful without an output schema, but the simplicity makes it acceptable.

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, each with clear descriptions (model ID, input token count, output token count). The description adds no additional parameter-level semantics beyond what the schema already provides, so the baseline of 3 applies.

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: 'Calculate cost from token counts for any supported AI model.' The verb 'calculate' is specific, the resource is well-defined (cost from token counts), and the scope ('any supported AI model') distinguishes it from sibling tools like budget_alert or cost_forecast, which handle broader forecasting or tracking.

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: when you have token counts and need a cost estimate. It doesn't explicitly compare to alternatives or state exclusions, but the 'Stateless' hint and focus on token counts provide clear context that this is for one-off calculations, not tracking or forecasting.

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