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laya-mcp

laya-mcp

by fuleinist
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

laya_decide

Run local decisions on text, email, tickets, or JSON by defining typed questions. Get probabilities and act/escalate signals in ~10-20 ms with no text generation.

Instructions

Ask the local Laya decision engine typed questions about a state (text, email, ticket or JSON). Each question is choice|score|noul; answers return probabilities plus an act/escalate signal in ~10-20 ms with no text generation. Define the answer space per call. Keep choice under ~20 options. Treat probabilities as hints until temperatures are refit on your own data.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "state": "<state>",
      "questions": "<questions>"
    },
    "name": "laya_decide"
  }
}

Input Schema

NameRequiredDescriptionDefault
stateYes
presetNo
questionsYes
timeout_msNo

Output 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 full burden. It discloses latency (~10-20 ms), the nature of output (probabilities, no text generation), and a calibration caveat. It does not mention side effects or permissions, but the wording 'ask ... about a state' implies a read-only, non-destructive operation.

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?

Every sentence earns its place: purpose, output type, performance, and usage constraints are packed into a compact, front-loaded description. No filler or redundancy.

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?

The tool has an output schema, so return-value detail is not required. The description covers core usage and constraints well, but the 'preset' parameter is completely undocumented in both schema and description, and 'noul' is unexplained. Given the tool's moderate complexity, this is a minor gap.

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 meaningful semantics for 'state' (text, email, ticket, JSON) and 'questions' (choice|score|noul, answer space per call, under 20 options). However, 'preset' and 'timeout_ms' are left unexplained, and 'noul' is undefined. This partial compensation warrants a middle score.

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 clearly identifies the tool as a decision engine for 'typed questions about a state' and enumerates supported state types. It is specific in verb and resource, though it does not explicitly contrast with sibling tools like laya_gate or laya_classify.

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?

It provides clear operational context: question types (choice|score|noul), output characteristics (probabilities, act/escalate signal, no text generation), and practical constraints (under ~20 choices, probabilities as hints). It does not name alternative tools or exclusion conditions, so not a 5.

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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