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Select optimal model

select_optimal_model

Selects the cheapest AI model for your task based on tier, capabilities, and budget, with full reasoning and fallback options.

Instructions

Pick the single cheapest model that meets the task tier, capabilities, and budget, with full reasoning and a fallbackChain. Offline.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskNoFree-text task; used for the tier heuristic when taskClass is absent.
taskClassNoExplicit task class; overrides the heuristic.
requiredCapabilitiesNoCapabilities the model must support.
maxCostUsdNoBudget ceiling for the predicted call cost.
estimatedInputTokensYesEstimated input tokens for the forecast.
estimatedOutputTokensYesEstimated output tokens for the forecast.
providersNoAxis 1: provider availability allowlist (spec 5.4).
targetNoAxis 2: "self" (default) applies the client scope; "code" considers all providers.
includeLocalNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
selectedYes
runnerUpNo
rejectedYes
reasoningYes
fallbackChainYes
budgetExceededNo
shortfallUsdNo
providerScopeNo
scopeSourceNo
cheaperIfAvailableNo
catalogVersionYes
asOfYes
Behavior2/5

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

Without annotations, the description only says it picks cheapest model with reasoning and fallbackChain; no details on side effects, auth requirements, or how 'offline' affects behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is very concise (one sentence) with no waste, though it could benefit from slight expansion.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having an output schema, the description is too brief for a complex selection tool; missing explanation of 'task tier', 'fallbackChain', and how inputs are used.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is high (89%), so baseline 3. Description adds only minor context (e.g., mapping task tier to parameters), not enough to raise score.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool selects the single cheapest model meeting task tier, capabilities, and budget, distinguishing it from siblings like compare_models or estimate_cost.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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; no when-not-to-use or when-to-use-this particular context provided.

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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