Calculates the cost of a single LLM request from supplied per-million token rates, including cached input tokens, to support budgeting and model comparison.
Estimate one LLM request from supplied per-million token rates. Input tokens include cached tokens.
| Name | Required | Description | Default |
|---|---|---|---|
| rates | Yes | ||
| record | Yes |
Changes observed during successful MCP inspections.
v0.1.2Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, and openWorldHint=false. The description adds useful calculation context by stating rates are supplied per-million tokens and that input tokens include cached tokens, but it does not explain how cached rates are applied, rounding, or what the estimate returns.
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 short sentences, front-loaded with the core action and followed by the key costing caveat. There is no wasted text.
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 a nested rates object, a nested record object, no output schema, and no schema descriptions. The description is too sparse to fully explain the required nested structure, currency, model-rate mapping, or the expected return, leaving significant gaps for an agent.
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 0%, so the description must carry parameter meaning. It clarifies per-million rates and cached-token inclusion, but leaves key nested semantics undocumented: rates.models keyed by model, currency format, asOf, requestHash, and outputTokens.
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?
States a specific verb and resource: 'Estimate one LLM request' cost from supplied token rates. It is clear what the tool does, though it does not explicitly distinguish itself from siblings analyze_llm_usage and compare_llm_models.
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?
There is no explicit guidance on when to use this tool versus the sibling tools, nor any when-not conditions. The phrase 'one LLM request' implies single-request estimation rather than bulk analysis, but that is left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.