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AgentKernel AI model data

Best AI models for a task

best_for_task
Read-onlyIdempotent

Ranks AI models for one task by performance only (price shown, never used for the rank). Tasks cover text, coding, agents, websites/UI/games/data viz, image generation and editing, video from text or image, video editing, text to speech, transcription, music and embeddings.

Input Schema

NameRequiredDescriptionDefault
taskYesTask slug
limitNo
licenseNoopen = open weights only, closed = proprietary onlyall

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true and openWorldHint=false, so the safety profile is covered. The description genuinely adds that price is displayed but excluded from ranking, which is non-obvious behavior, but says nothing about response shape, ordering details, or how ties are handled.

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 the core ranking behavior in the first clause, then a single dense sentence enumerating covered task domains. Every part earns its place, though the long taxonomy list reads as a run-on rather than an optimized structure.

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

Completeness3/5

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

With no output schema, the description carries the burden of describing returns, and 'price shown' is the only hint about response contents; ordering, count, and result fields are unaddressed. The limit and license filters go entirely unmentioned, so a three-parameter tool is only partially covered.

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 description coverage is 67%, with the license parameter already documented in the schema and task's enum giving the allowed slugs. The prose enumerates task families (text, coding, agents, image/video, audio, embeddings) but omits 'overall' from the enum and never mentions the limit or license parameters, so it only partially compensates.

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

Purpose4/5

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

The description states a specific verb and resource ('Ranks AI models for one task') with a clear scope modifier ('by performance only'). The parenthetical carve-out that price is shown but never used for ranking effectively separates it from cheaper_alternative, though no sibling is named explicitly.

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

Usage Guidelines3/5

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

The phrase 'by performance only (price shown, never used for the rank)' implies that this is the performance-first tool as opposed to price-oriented siblings, but the agent has to infer that routing. There is no explicit 'use X when Y' statement or exclusion of alternatives.

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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