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Laya Serve MCP
by enlotec

laya_predict

Submit typed choice, score, and yes/no questions to the Laya model to get predictions.

Instructions

Answer typed choice, score, and yes/no questions using Laya.

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_predict"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelNo
stateYes
questionsYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.3/5.0
Behavior2/5

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

With no annotations, the description carries the full behavioral burden, and it discloses almost nothing: not whether the call is a read-only inference or mutates server-side state, not whether data is sent to an external Laya service, and not what latency or limits apply. The existence of an output schema excuses it from explaining return values, but the operational profile remains opaque.

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

Conciseness3/5

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

It is a single front-loaded sentence with no filler, so there is nothing to trim. The problem is under-specification rather than verbosity, which caps this dimension in the middle.

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

Completeness1/5

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

This is a tool with nested object parameters, zero schema coverage, no annotations, and two required inputs, yet the description is one short sentence. An agent cannot construct a valid 'state' or 'questions' payload from what is given, so the definition is not complete enough for correct invocation.

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% and there are three parameters, including a nested free-form 'questions' object and an undocumented 'state' object. The mention of choice, score, and yes/no question types hints at the shape of the questions payload, but nothing explains 'state' or the role of the optional 'model' parameter.

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

Purpose3/5

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

The description names the operation ('Answer') and the kinds of questions handled (typed choice, score, yes/no), which is more than a tautology. However, it never states what is being predicted or over what domain, and it does not differentiate the tool from its sibling laya_status.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus alternatives, no prerequisites such as authentication or model selection, and no indication of what inputs are appropriate. The agent must infer all usage context from the name alone.

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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