ai-rate-limit-tracker

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

The description says 'Forecast' and 'get scheduling recommendations', implying read-only behavior, but lacks detail on side effects, authentication requirements, or what data is used. No annotations exist to clarify further.

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 and front-loaded with the action, but it is somewhat vague and could benefit from a bit more context.

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 4 parameters and no output schema, the description does not explain what the forecast output looks like, how to interpret recommendations, or any prerequisites. It is insufficient for an agent to fully understand the tool's 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?

Schema coverage is only 25% (only rate_rpm has a description). The description does not add meaning to the other parameters (api_key, provider, model) beyond their names, leaving the agent to infer their purpose.

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 the tool forecasts rate limit exhaustion and provides scheduling recommendations. This distinguishes it from siblings like get_provider_limits (which retrieves limits) and track_usage (which tracks current usage).

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 alternatives. There are no explicit when-to-use or when-not-to-use conditions, nor any suggestions for which sibling might be more appropriate.

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 full burden for behavioral disclosure. It effectively communicates the return values (RPM, TPM, RPD, context window) and implies a read-only operation. However, it does not mention potential rate limits on the tool itself, authentication needs, or any side effects. Still, it provides good transparency for its scope.

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 conveys the core purpose efficiently. It is front-loaded with key information and contains no filler. It earns a high score for brevity, though it could optionally include a usage note without sacrificing 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?

Given the tool's simplicity (2 parameters, no output schema, no nested objects), the description is sufficiently complete. It explains what data is retrieved and for what inputs. It doesn't cover return format or pagination, but for a rate-limit lookup this is likely unnecessary. It meets the needs of the expected use case.

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 complete descriptions for both parameters ('provider' with enumerated list, 'model' as optional string). The description adds no additional meaning beyond restating the schema's information. Baseline score 3 is appropriate since schema coverage is 100%.

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 ('Get') and clearly identifies the resource ('published rate limit specs including RPM, TPM, RPD, context window') and scope ('for an AI provider and optional model'). It distinguishes from sibling tools like get_forecast which deals with forecasts, not rate 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 explains what the tool does but does not provide explicit guidance on when to use it versus alternatives. It lacks conditions, prerequisites, or exclusions. For a straightforward lookup tool, the context is implied but not explicitly 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 carries the full burden. 'List' implies a read-only, non-destructive operation. The description adds context about the output content (models and default limits), which is beyond what annotations would typically provide. It could be more explicit about read-only safety, but the implication is strong.

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 efficiently communicates the tool's purpose and output. No extraneous information. Front-loaded with the key action and resource.

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 no parameters, no output schema, and no annotations, the description provides sufficient context by specifying it lists providers, models, and default limits. It could mention the return format but is adequate for a listing 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?

There are no parameters, so schema description coverage is 100%. The description does not need to add parameter semantics. A baseline of 3 is appropriate for high coverage, but the absence of parameters means the description adds no redundant information, warranting a 4.

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 ('List all AI providers') and the specific resource ('supported by the tracker'). It distinguishes from sibling tools like get_forecast and get_provider_limits by focusing on the provider list with models and 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 implies the tool should be used to retrieve an overview of all providers, their models, and default limits. While it doesn't explicitly state when not to use it or provide alternatives, the context is clear enough given the tool's straightforward listing function.

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; description only covers recording and returning counters. Missing side effects, permissions, duplicate handling, or rate limits.

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, front-loaded action and result. Efficient but could be slightly expanded without losing 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, 5 params, no annotations. Description doesn't cover return format, errors, prerequisites. With siblings, more context needed for full 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?

Only api_key has schema description (20% coverage). Description adds no parameter meaning for model, provider, latency_ms, tokens_used. Does not compensate for low schema 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?

Description clearly states action (record call), resource (AI API call), and result (counters, warnings). Differentiates from siblings: get_forecast, get_provider_limits, list_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?

No explicit when-to-use or when-not-to-use. Siblings exist but no comparison or mention of when to prefer track_usage over get_provider_limits for limit warnings.

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