ai-rate-limit-tracker

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

With no annotations provided, the description carries the full burden for behavioral disclosure. It mentions the tool forecasts and recommends, but does not describe whether the operation is read-only, what data it relies on, whether any side effects occur, or the nature of the scheduling recommendations. This is a significant gap for a forecast 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 sentence that is direct and front-loaded with the verb 'Forecast.' It avoids fluff and excess wording, making it concise. However, its brevity sacrifices necessary detail, though for conciseness alone it earns a high score.

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 no output schema, no annotations, and only partial parameter documentation. The description does not explain what the scheduling recommendations look like, what inputs are required (beyond the schema), or what prerequisites exist (e.g., previous usage data). It is under-specified for an AI agent to invoke the tool effectively.

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 only 25% (only rate_rpm has a description), and the tool description adds no meaning to the parameters api_key, provider, model, or rate_rpm. It does not explain their roles or how they affect the forecast. The description fails to compensate for the sparse 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 tool's function: to forecast when rate limits will be exhausted and provide scheduling recommendations. This action is distinct from sibling tools like get_provider_limits (which lists limits) and track_usage (which tracks usage), making the purpose unambiguous.

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 used when you need to predict rate limit exhaustion, but it does not explicitly state when to use it versus alternatives like track_usage or get_provider_limits. There is no 'use this when' or 'instead of' guidance, leaving the decision to inference.

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' and identifies the resource type, but it does not explicitly state that it is safe, whether it makes external calls, or how errors are handled. 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, well-structured sentence that is front-loaded with the main purpose and includes relevant detail. There is zero redundancy or unnecessary 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 tool is simple with only 2 parameters, but there is no output schema. The description lists the data fields returned but does not specify the response structure, pagination, or error behavior. This leaves some ambiguity about the exact format of the output, warranting a moderate score.

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 reiterates that model is optional and mentions the output fields (RPM, TPM, RPD, context window), but it does not add significant meaning beyond the schema for individual parameters.

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 with a specific verb ('Get') and resource ('rate limit specs'), and lists concrete fields (RPM, TPM, RPD, context window). It distinguishes itself from sibling tools like get_forecast and track_usage by focusing on published limits rather than predictions or usage 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 provides clear context for when to use the tool: whenever rate limit specifications are needed for a provider and optionally a model. However, it does not explicitly mention alternatives or when not to use this tool, falling short of the highest bar for usage 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 provided, the description carries the burden of behavioral disclosure. It explicitly states what the tool returns ('models and default limits'), which implies a read-only listing operation. This is sufficient for a simple list tool; no hidden side effects are hinted at.

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 front-loads the purpose ('List all AI providers') and appends the relevant output details. No wasted 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?

For a parameterless list tool with no output schema, the description fully specifies the output scope (all providers, models, default limits). There is no ambiguity or missing important 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?

The tool has zero parameters, so schema coverage is effectively 100%. The baseline for no parameters is 4, and the description correctly doesn't attempt to explain parameters that don't exist.

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 'List' and clearly identifies the resource ('all AI providers supported by the tracker') and the included details ('models and default limits'). It naturally distinguishes from siblings like get_provider_limits, which likely targets a specific provider's limits.

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 establishes a clear context: it is for listing all providers at once. There are no explicit exclusions or alternative recommendations, but the scope is unambiguous enough for an agent to infer when to use it. Sibling names further reinforce the 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 must carry the full behavioral burden. It only mentions recording and getting counters/warnings, but does not disclose side effects (e.g., persistent record creation), authentication requirements, error behavior, or rate limits. This leaves significant ambiguity for an agent.

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 primary action and outcome. No filler words or redundant information. It earns a high score for efficiency.

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 has 5 parameters, no output schema, and no annotations, the description is insufficient. It does not explain the return shape of 'usage counters plus warnings', nor provide prerequisites or parameter usage guidance. An agent would need to infer too much from the schema alone.

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 only 20% (only api_key has a description). The tool description does not compensate for the other parameters (model, provider, latency_ms, tokens_used) by explaining their roles or relationships. It merely says 'AI API call', which is too vague to substitute for 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 a specific verb ('Record') and resource ('an AI API call'), and clearly distinguishes itself from sibling tools that are read-only (get_forecast, get_provider_limits, list_providers) by emphasizing action and real-time feedback. It states the primary function and outcome in one sentence.

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 context is clear: use this tool when you need to record an AI API call and receive usage feedback. However, it does not explicitly mention alternatives or when not to use it, relying on the sibling names to imply differentiation. This is acceptable but lacks explicit exclusions.

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