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get_fastest

Retrieves the top N AI models with the lowest latency by pinging configured providers. Supports filtering by tier, provider, count, free only, and verification.

Instructions

Get the N fastest available models right now. Use when the user wants recommendations or "best/fastest/free" models.

Pings configured providers and returns top N by latency. Use model_id from results as the code name when inserting or configuring (e.g. run(prompt, model_id=..., provider=...)). Example: get_fastest(free_only=True, min_tier="A", count=5) for "5 free A-or-better models".

When verified=True, also sends a real prompt to each model to confirm it produces non-empty output. Models that ping as UP but return empty content are excluded.

Args: min_tier: Minimum quality tier (default "A" — shows S+, S, A+, A) provider: Limit to specific provider count: How many results (default 5) free_only: If true, only return models marked as free verified: If true, verify models produce non-empty output (default false)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNo
min_tierNoA
providerNo
verifiedNo
free_onlyNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Describes pinging providers, returning top N by latency, and the verified parameter behavior (sends real prompt, excludes empty outputs). No annotation contradictions. Could mention rate limits or auth, but sufficient.

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?

Front-loaded with purpose, followed by usage guidance, example, and parameter details. Every sentence is informative. Slightly long but justified by parameter count.

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

Completeness4/5

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

Given output schema exists, description need not explain return values. Covers purpose, usage, parameters, and behavioral details adequately for a tool with 5 parameters and no annotations.

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

Parameters5/5

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

Schema coverage is 0% (based on context signals), but description provides thorough explanations for all 5 parameters including defaults, meanings, and an example. Adds significant value beyond schema.

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?

Clearly states verb (get), resource (fastest available models), and scope (N fastest, right now). Distinct from siblings like list_models due to focus on speed and recommendation.

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

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says when to use ('when user wants recommendations or best/fastest/free models'). Includes an example. Does not explicitly mention when not to use or alternatives, but guidance is clear.

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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