Cheapest blended $/1M at each capability level (Epoch ECI 130/145/160) for closed models, open models at their lab's price and open models at the cheapest third-party host; the open discount; DeepSeek's floor lift (cheapest price without DeepSeek / cheapest overall, at lab and at host prices); and the host discount per open model (lab price / cheapest host).
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It usefully discloses the formulas behind the comparison metrics, such as lab price divided by cheapest host and cheapest non-DeepSeek price divided by cheapest overall, but it does not describe the return shape, whether results are a table or scalar values, or any operational constraints.
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 content is relevant but packed into a single long sentence with multiple parenthetical metric definitions. It is not front-loaded with a high-level summary and could structure the list of computed metrics more clearly.
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 zero-parameter analytical tool with no output schema and no annotations, the description explains the metrics but omits the return format and when-to-use context. It is minimally adequate for understanding the calculations, but not complete enough for confident tool selection.
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 and an empty input schema, so there are no parameter semantics to explain. The baseline score of 4 applies because the description does not need to compensate for undocumented inputs.
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 names a specific comparative analysis: cheapest blended $/1M across closed vs open models, capability tiers, host discount, and DeepSeek floor lift. However, it is a dense noun phrase rather than a clear verb-led purpose statement, and it never distinguishes this tool from siblings such as model_price, cost_per_task, or index_latest.
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?
It describes the outputs the tool computes but gives no explicit guidance on when to use this instead of model_price, index_latest, cost_per_task, or other pricing/comparison siblings. There are no exclusions, prerequisites, or alternative-selection cues.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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