Get published rate limit specs (RPM, TPM, RPD, context window) for an AI provider and optional model.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | Model name (optional) | |
| provider | Yes | openai | anthropic | gemini | groq | cohere | mistral | together | deepseek |
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.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: listing providers, fetching limits, recording usage, and forecasting. No overlap exists between them.
All tool names follow a consistent verb_noun snake_case pattern (list_providers, get_provider_limits, track_usage, get_forecast).
With 4 tools, the server is well-scoped for its purpose. Each tool addresses a core aspect of rate limit tracking without unnecessary bloat.
The surface covers discovery, limit specs, usage recording, and forecasting. A minor gap is the lack of historical usage queries or reset functionality, but the core workflow is complete.