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laya_tell_me

Run bounded classifications, ordered scoring, and yes/no probability assessments locally with Laya-MLX. Provide state and questions to get advisory results for decision support.

Instructions

Run a local typed decision with Laya-MLX.

Use for bounded classification, ordered scoring, and yes/no probability. Question types are choice, score, and noul. Treat results as advisory; never use them as authorization for destructive or consequential actions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stateYes
questionsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses that results are advisory and not authorization for destructive actions, which is valuable behavioral context. However, it doesn't describe any other behavioral traits such as potential side effects, error handling, or whether the operation is synchronous or has latency. The advisory warning is a positive, but it stops short of a comprehensive disclosure.

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

Conciseness5/5

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

Three sentences, each earning its place: the first states the core action, the second gives usage scope, the third is a safety caveat. No filler or repetition. The most important information is front-loaded.

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?

Given two parameters, nested objects, and an output schema (which is not shown), the description should explain how to structure inputs. It doesn't describe the 'state' or 'questions' format beyond mentioning question types. While the output schema might clarify returns, the input side is underspecified, making the tool hard to use correctly without external knowledge. The advisory note adds safety context but doesn't fill the parameter gap.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate. It mentions 'question types are choice, score, and noul', which hints at the structure of the 'questions' parameter but doesn't explain how to encode them or what 'state' should contain. Neither parameter is explicitly described, leaving an agent without enough information to construct valid inputs. This is a significant gap.

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 clear verb ('Run') and resource ('a local typed decision with Laya-MLX'), and enumerates the decision types (bounded classification, ordered scoring, yes/no probability). It doesn't explicitly differentiate from siblings (none exist), but the purpose is understandable and not a tautology. A slight vagueness in 'local typed decision' prevents a 5.

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?

The description explicitly says 'Use for bounded classification, ordered scoring, and yes/no probability', giving clear when-to-use guidance. It also includes a caution against using results for destructive actions, which is a form of when-not-to-use. Since there are no sibling tools, it can't name alternatives, but the guidance is otherwise explicit and practical.

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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