modelsLists the LLMs you enabled in Leanfare with their USD price per 1M tokens and Arena quality rating, optionally filtered by provider. Use it to pick affordable, capable ones.
The models the user ticked in Leanfare and can use now, with their prices in US dollars per 1M tokens and their quality rating (Arena).
| Name | Required | Description | Default |
|---|---|---|---|
| provider | No | Only this provider, for example openai, anthropic, google, openrouter. |
Changes observed during successful MCP inspections.
v0.15.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description does add real behavioral context beyond them: results are limited to the user's ticked models, and each entry carries a USD-per-1M-token price plus an Arena rating. It does not say what happens when the user has ticked nothing or how large the result set typically is.
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?
One compact sentence that front-loads the resource and then the payload (prices, rating). Nothing is padded, though the sentence is slightly dense and could be split to separate scope from return fields.
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-required-parameter read-only lookup with no output schema, the description covers the essential questions: which models (the user's ticked, usable ones), and what comes with them (price and quality rating). Only the output shape and the provider filter are unaddressed, both minor here.
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% and the single optional 'provider' parameter already documents its accepted values with examples. The description never mentions the provider filter, so it adds no meaning beyond the schema; baseline 3 applies.
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 states a concrete resource and scope: the models the user has ticked and can currently use, returned with per-1M-token USD prices and an Arena quality rating. That is specific enough for an agent to know what it gets back, though it never uses an explicit verb like 'list' and does not distinguish itself from the related 'local_model' or 'level' siblings.
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 when-to-use or when-not-to-use guidance and no mention of alternatives such as 'estimate' or 'local_model'. The agent must infer that this is the lookup step before pricing or planning, which is plausible but never stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.