Skip to main content
Glama
fuleinist

laya-mcp

by fuleinist

laya_classify

Classify a batch of text items against a fixed catalog of up to 20 labels, returning per-item labels with confidence scores.

Instructions

Classify many items against one shared catalog in a single forward pass (catalog <= 20 labels). Batched cost is ~2-5 ms per item, cheaper than one LLM reasoning turn for any dedupe / triage / labelling sweep. Returns the label per item with confidence.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsYes
catalogYes
timeout_msNo
instructionsNoWhich category does each item belong to?

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral disclosure burden and does so substantively: single forward pass, catalog cap of <=20 labels, ~2-5 ms per item cost, and label-with-confidence output. It does not discuss failure modes or side effects, but for a classifier this is meaningful transparency.

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?

Two tight sentences front-load the core operation and constraint, then add cost and output details. Every clause contributes useful information with no filler or repetition.

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 the output schema exists and the schema provides defaults for timeout_ms and instructions, the description is largely complete: it states what the tool does, when to use it, its constraints, cost, and output. The main gaps are catalog structure and explicit parameter guidance, but these are partially covered by the input schema.

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 0%, so the description must compensate. It adds meaning to 'catalog' (shared, <=20 labels) and 'items' (many items classified per call), but it never mentions timeout_ms or instructions, and the catalog object's structure is left to schema inference.

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 opens with a specific verb and resource: 'Classify many items against one shared catalog.' It further distinguishes itself from siblings by emphasizing batched, single-pass classification with a label limit and per-item confidence output.

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 description gives a clear intended use case ('dedupe / triage / labelling sweep') and a cost-based rationale for choosing it ('cheaper than one LLM reasoning turn'). However, it never explicitly names sibling tools or states when not to use it, and mentioning 'triage' creates potential overlap with the sibling laya_triage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.