ai-cost-optimizer

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

No annotations are provided, so the description must fully disclose behavior. It mentions raising a flag on threshold, but does not explain what 'flag' means (e.g., error, notification, log), potential side effects like modifying configuration, or required permissions. The description is insufficient for a tool with no annotations.

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?

Single sentence that efficiently states core functionality. No wasted words. Could benefit from a brief breakdown of actions, but overall well-structured for conciseness.

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?

No output schema is provided, so the description should explain what the tool returns (e.g., status, alert details). The phrase 'raises a flag' is vague. Without output info, the agent cannot anticipate the return format. Additionally, sibling context is not leveraged to differentiate 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?

Input schema has 100% description coverage for all 4 parameters. The description adds minimal additional meaning beyond the schema (e.g., 'configurable spending threshold' maps to threshold). Baseline 3 applies; no extra 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 clearly states the dual purpose of setting and checking budget limits, and raising alerts on thresholds. It distinguishes from siblings like cost_forecast (predictive) and cost_tracker (monitoring), but does not explicitly call out when to use each.

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 the listed siblings (cost_forecast, cost_tracker, etc.). The agent must infer from the description. Explicit usage context is missing.

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 only states the tool 'forecasts' but does not disclose if it is read-only, any side effects, or authorization requirements. It lacks sufficient behavioral context.

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?

A single, front-loaded sentence that is clear and concise with no extraneous information.

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 explains the forecasting method but does not mention the output format or any caveats, which is notable given the absence of an output schema. Overall adequate for a simple 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 coverage is 100% with clear parameter descriptions for team, forecastDays, and historicalDays. The description adds no additional meaning 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 forecasts future AI spend using daily-average linear projection, distinguishing it from sibling tools like budget_alert and cost_tracker.

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 forecasting based on historical patterns but does not specify when to use or when to prefer alternatives like budget_alert or model_breakdown.

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 full burden. It discloses persistence to Cloudflare KV, implying statefulness and durability ('survives cold starts and scale-out'). However, it omits details like idempotency, rate limits, or destructive behavior, which are partially mitigated by the tool's simple nature.

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?

Two sentences front-loaded with the core purpose and a key behavioral trait. Zero waste; every word earns its place.

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 output schema and a moderate number of parameters (6), the description covers the primary functionality and persistence. It lacks details on return format or scope of 'recent records', but the tool is simple enough that this is 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?

Schema description coverage is 100%, so the baseline is 3. The description ('by team') reinforces the 'team' parameter but adds no new meaning beyond what the schema already provides for each parameter.

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 explicitly states 'Record and retrieve AI API call costs by team', which is a specific verb+resource. It clearly distinguishes from sibling tools (budget_alert, cost_forecast, model_breakdown, token_calculator) by focusing on recording and retrieval of costs.

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 team-level cost tracking but provides no explicit guidance on when to use this tool versus alternatives (e.g., when to use budget_alert or cost_forecast). No when-not-to-use or context exclusions are given.

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 provided, so description must carry full burden. It describes the function but does not disclose behavioral traits such as read-only nature, authentication requirements, rate limits, or data freshness. For a simple analysis tool, the lack of explicit safety hints is a 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?

Single sentence with no wasted words. The information is front-loaded 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?

Covers the essentials: what it does and main parameters. However, it lacks details about return format, pagination, or error handling. Given the tool's simplicity, it is adequate but not 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?

Schema coverage is 100% with descriptions already explaining both parameters (days and team). The description adds the context of 'configurable time window' but does not significantly enhance understanding beyond 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?

Description clearly states verb 'analyze', resource 'costs broken down by model', scope 'for a given team' and time window. This distinguishes it from sibling tools like cost_forecast or budget_alert which are for forecasting or alerts.

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?

Provides clear context for when to use (cost analysis by model, team, time window) but does not explicitly mention when not to use or alternatives. However, the context itself guides usage.

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 full burden. It discloses statelessness and no KV requirement, which are useful behavioral traits. But it omits details like whether the tool validates model support, error behavior for unknown models, or 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?

Two sentences, each earning its place. The first states purpose, the second adds behavioral context. 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?

Given 3 parameters, no output schema, and no annotations, the description is adequate but could be improved by stating the return type (e.g., 'Returns the cost in USD') or listing supported models. It covers the gist but lacks completeness for an agent invoking 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 coverage is 100%, so parameters are well-documented in the schema. The description adds marginal value by stating 'any supported AI model' for the model parameter, but doesn't elaborate on token count formats or constraints beyond what the schema 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 'Calculate cost from token counts for any supported AI model' uses a specific verb ('calculate') and resource ('cost from token counts'), clearly distinguishing it from siblings like budget_alert or cost_forecast which deal with budgets or forecasts.

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 mentions 'Stateless — no KV required,' implying it's safe and simple to call without side effects or prerequisites. However, it does not explicitly state when to avoid this tool (e.g., for non-supported models) or provide alternatives beyond sibling context.

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